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Hi, I am Ken Suzuki, a third-year undergraduate mechanical engineering student from the University of Massachusetts Amherst, located in the United States. I joined the internship program at OMRON SINIC X (OSX) in Tokyo, Japan, from June 1st through August 31st of 2023.

At OSX, I was involved in the development of a shape memory alloy (SMA) actuator, under the supervision of Dr. Kazutoshi Tanaka and Dr. Masashi Hamaya of OMRON SINIC X. I would like to express my tremendous gratitude to Dr. Kazutoshi Tanaka Dr. Masashi Hamaya for giving me the opportunity to be a part of the internship program at OMRON SINIC X. I am very grateful for their invaluable advice and supervision, and their support allowed me to grow as a mechanical engineering student and aspiring roboticist.

In this report, I will share what I worked on during my three-month internship period, including my design process and background information about shape memory actuators from various research papers and other reputable online sources. In the end, I will also share my thoughts about interning at OMRON SINIC X.

## How Shape Memory Alloys Work
A shape memory alloy, or an SMA for short, is a special type of alloy that is characterized by its ability to demonstrate a “shape memory effect” [9]. This effect is caused by phase transformations in the crystal structure of the alloy that allow it to transform into any memorized shape [9]. In the case of our project, we utilized Nitinol (NiTi) wire. Nitinol wire has a special property, where the wire contracts when it is heated, and then transforms back to its original length when it is cooled down [9].

![image](https://miro.medium.com/v2/resize:fit:640/format:webp/1*xtyQAwj2ADzt4S__iWaHLw.png)
How an SMA wire contracts (Diagram drawn by Ken Suzuki)

## Advantages/Disadvantages of Using Shape Memory Alloys
SMA actuators have favorable advantages compared to traditional methods of actuation: higher power density (high output to weight ratio), compact size, silent actuation, and lightweight form factor [1]. However, SMA actuators also hold many disadvantages. When actuated, SMA wires exhibit nonlinear behavior when comparing their linear displacement with temperature [3]. They are also subject to hysteresis effects, where transformation temperatures between different phases depend on the history of the transformation [9]. This makes SMA actuators difficult to control.

SMAs come in different shapes in sizes, but typically come in the form of thin wire. SMA wires mainly come in two categories, straight wires or coiled wires, where coiled wires have higher contraction compared to their straight counterparts [6]. Coiled SMA wire can contract by lengths of approximately 46% in comparison to their original length [6]. Although coiled SMA wires have the advantage of having higher rates of contraction, they have the drawback of having a much lower pulling force than straight SMA wires [3]. Therefore, it is desirable to use straight SMA wires when a higher pulling force is needed.

In order to preserve the benefit of having higher pulling force in straight SMA wires while maximizing the total contraction length, the straight SMA wire must be made as long as possible, since the total contraction of an SMA wire increases as the total length of the wire increases. There have been various methods of increasing the total wire length, while still arranging the wires to fit into a small space. Ballester et. al presented a method of arranging SMA wires using pulleys [2]. Copaci et. al used a method of arranging SMA wires using a Bowden cable [4]. These methods of arranging SMA wires do indeed solve the problem of a long strand of SMA wire taking up too much space, but utilizing these methods of arrangement causes friction between the SMA wires, or in case of the Bowden cable, unnecessary heat accumulation, which can cause losses in both actuation time and tension [4, 10].

## Methods for Actuating Shape Memory Alloys
The first approach we took for actuating the SMA wire, proposed by former OSX intern Temma Suzuki, was to heat it up using Joule heating, which involved passing an electric current through the wire. The wire would then be cooled down using natural air convection. This method of actuating SMA wires has been widely researched, and it has the advantage of being extremely lightweight and compact. However, Joule heating presents some issues when trying to actuate the SMA wire at a high frequency. Firstly, the rate of heat dissipation in natural air convection is not as efficient when compared to other fluids (i.e., water), which slows down the cooling time [3]. The rate of cooling of the SMA wire is also dependent on the temperature of the surrounding air, which makes it difficult to cool the wire at a consistent rate [3]. Another issue with using Joule heating was the tremendous amount of electric current required to actuate the SMA wires. For instance, a single 0.1 mm diameter SMA wire requires 300 mA of current to actuate [5].

The second approach we took was to actuate the SMA using temperature-controlled water. Park et. al showed in their research that actuating SMA wires using temperature-controlled water yields a maximum actuation frequency of 1Hz [3]. This method of actuation also has the advantage of far more efficient cooling of the SMA wire compared to natural convection with air, since water has a significantly lower heat capacity than air. This method also eliminates the need to run a large current through the SMA wire.

## Design Approach, and (Incomplete) Assembly
The primary goals for the design of the SMA actuator were to match the hardware specifications of the MIT Humanoid robot from the Biomimetic Lab at MIT [7], with the ultimate goal being making an SMA-actuated humanoid robot that can jump. For the SMA actuator to meet the specifications of the MIT Humanoid robot, the following hardware elements were held paramount: high force, high actuation frequency, fast speed of contraction, compact size, and low weight. To make the SMA actuator as compact as possible, the SMA wires must be fixed as densely as possible. We opted to use the 100μm diameter NT-TTR wire from FURUKAWA TECHNO MATERIAL CO., LTD [5]. According to experiments conducted on an SMA actuation testbed that employs Joule heating, developed by former OSX intern Temma Suzuki, the NT-TTR SMA wire has a maximum tension force of around 140 grams and a maximum contraction speed of 16 mm/s (for a 14 cm long wire). We first made the goal of using 1000 of these SMA wires in our actuator, which would equate to a pull force of approximately 1373 N.

![image](https://miro.medium.com/v2/resize:fit:592/format:webp/1*gntT98dSSjZ8X4240UbPrQ.png)
SMA wire contraction speed, plotted in Matplotlib

![image](https://miro.medium.com/v2/resize:fit:640/format:webp/1*s3YucliJlvF7Z2ihklgk-g.png)
Measuring the tension force in a single SMA wire, using Temma Suzuki’s SMA testbed (Picture by Ken Suzuki)

Since actuating 1000 SMA wires using electricity would require 300 A of current to activate all the wires, we decided to use temperature-controlled water to control the SMA actuator, like Park et al’s work [3]. For a water temperature controlled SMA actuator to be used on a robot, there needs to be a water circulating device that can supply temperature-controlled water to the SMA actuators. To simulate this, a water circulation system was designed using an Arduino microcontroller, solenoid valves, and water pumps. The water circulation system was inspired by a water temperature controller from a thermal display in Gallo et. al’s work [8]. A hot water kettle and an ice bucket was also prepared to provide hot and cold water required for SMA actuation. To mount the SMA wires, the following linear-guide apparatus was designed.

![image](https://miro.medium.com/v2/resize:fit:640/format:webp/1*MGa9Qf6Jh7tmZHiWR8El6g.png)
Linear guide apparatus with SMA actuator, screenshot from SolidWorks (model created by Ken Suzuki)

The SMA wires are mounted to a top plate with holes placed very densely. The SMA wires are then subsequently glued into the hole, thus fixing the wires.

![image](https://miro.medium.com/v2/resize:fit:640/format:webp/1*YtPdxMN3_lM1OlV2NrrOVw.png)
View of SMA wires, with tube removed, in SolidWorks model

![image](https://miro.medium.com/v2/resize:fit:640/format:webp/1*7fCm1n6ccXv8kg9cEvmL9Q.png)
Exploded view of the SMA holding plate and fittings

The apparatus is then wrapped in a large silicone tube, sealing the fittings on both ends, and thus, allowing water to flow through. The slider is then connected to a bias spring, which supplies the SMA wires with enough load stress to undergo contraction.

![image](https://miro.medium.com/v2/resize:fit:640/format:webp/1*xdkRAc2BYEWRrLqFAZK2dw.png)
Side view of linear guide apparatus with bias springs, for providing bias force to the SMA wire

## Assembling the Testbed
Since the internship period was not very long, the testbed was unfortunately not fully assembled at the end. The following is a sneak peak of the assembly process.

The assembly of the SMA actuator involved a process of gluing each SMA wire onto the SMA holding plate. The individual SMA wires were first passed through an arbitrarily selected hole on one of the top plates, then glued in place. Subsequently, the other side of the wire was then glued into the corresponding hole on the top plate of the opposing side. This was an incredibly tedious process.

![image](https://miro.medium.com/v2/resize:fit:640/format:webp/1*USt1aaLq1z2j1F1Kc9uRCQ.png)
Passing each SMA wire into the holes of the holding plate (Picture by Ken Suzuki)

![image](https://miro.medium.com/v2/resize:fit:640/format:webp/1*J-cHKOqRNelpOArH1gTuOQ.png)
Gluing the SMA wires into the top plate (Picture by Ken Suzuki)

![image](https://miro.medium.com/v2/resize:fit:640/format:webp/1*AKev60MtvW5QlJcAQvb5QA.png)
Arranging the SMA wires (Picture by Ken Suzuki)

![image](https://miro.medium.com/v2/resize:fit:640/format:webp/1*fGpGnvsbafOc3l_1ObidjQ.png)
Arrangement of SMA wires, close up (Picture by Ken Suzuki)

After the individual SMA wires were glued in place, a silicone tube to seal the SMA wires from water was made using a thin sheet of silicone, 0.5 mm in thickness. The thin sheet of silicone was initially cut to size using a knife, rolled into a hose shape, then glued together using silicone sealant, and left to dry for around 30 minutes. The glued silicone sheet was then fixed to the SMA actuator using hose bands.

![image](https://miro.medium.com/v2/resize:fit:640/format:webp/1*JC6m1Yp_wzgAqLrEKcqF9w.png)
Silicone sheet being glued together using silicone sealant (Picture by Ken Suzuki)

![image](https://miro.medium.com/v2/resize:fit:640/format:webp/1*9bUAHw1xH-yHy8R6oFoyaw.png)
SMA actuation testbed (Picture by Ken Suzuki)

The testbed was planned to be controlled by solenoid valves. The diagram of the flow of water is shown below.

![image](https://miro.medium.com/v2/resize:fit:604/format:webp/1*qRTLMuiFUPv5xTUjfSz6hw.png)
Diagram of water circulation system (Diagram by Ken Suzuki)

![image](https://miro.medium.com/v2/resize:fit:606/format:webp/1*qSNmLHN7qAqfcK90yEzfZQ.png)
Actual water circulation system (Picture by Ken Suzuki)

An Arduino microcontroller was used to control the flow of water. The circuit diagram is shown below.

![image](https://miro.medium.com/v2/resize:fit:594/format:webp/1*A32ZWlug2BvVB1BZYB5fxw.png)
Rough diagram of Arduino circuit (Drawn by Ken Suzuki)

![image](https://miro.medium.com/v2/resize:fit:640/format:webp/1*7wGe7Na8s8XXR5SjBA6gHQ.png)
Circuit with messy wiring (Picture by Ken Suzuki)

In the end, we conducted an experimental run of the testbed. We discovered that the hose band was not sufficient at securing the silicone tube, which resulted in water leakage from a minor gap between the fitting and the silicone sheet held in place by the hose band. This marked the end of the internship.

## Conclusions and Future Works
Due to time constraints that caused the project to be unfinished, this project is still a work in progress. Further research is needed for coming up with a way to implement this type of actuation system in a humanoid robot. For future works, the following things can be considered:

To utilize a temperature-controlled water SMA actuator in a robot, there must be a system to supply the temperature-controlled water to the SMA in a timely manner. Gallo et. al developed a water temperature control system, where water from a hot water tank and a cold water tank were mixed to output water of a desired temperature [8]. In their water circulation system, the temperature in each tank was controlled using a Peltier element and a set of heat exchangers [8].

An automatic temperature controlling water circulation device, similar to the one used in Gallo et. al’s work can be implemented into a robot. This circulation system would supply the temperature-controlled water to the SMA actuator for robot movement. For a water temperature control system like the one listed above to be implemented in a legged robot, total weight and size must be considered in the design to keep the actuator from hindering the robot’s dynamical performance. Ideally, the temperature-controlled water SMA actuation system would be as lightweight and compact as possible.

A pump that can generate just the right amount of water pressure must also be found, while maintaining a compact and lightweight formfactor. The pump that was purchased for the prototype SMA actuator testbed (EZO-PMP from Atlas Scientific) did not create adequate pressure to provide enough flow of water.

## My Thoughts About the Internship OMRON SINIC X
Being an intern at OMRON SINIC X provided me the chance to live and work in Japan for an extended period of time, which also happened to be my first experience living in Japan for a long duration. Through the internship program, I was able to get a glimpse of what it is like to work in Japan. The internship also allowed me to gain real-world engineering experience and exposure to soft robotics research, which gave me the opportunity to apply what I have learned in my mechanical engineering curriculum from a school-setting to the real-world.

Throughout the internship, Dr. Tanaka and Dr. Hamaya provided great encouragement, which has further inspired me to pursue a career in robotics research. Not only do they help me cultivate my creativity and innovation skills, but they also give me the chance to foster my collaborative skills, which are extremely valuable in research.

This internship allowed me to challenge myself, and it especially taught me how to thrive in a research environment. OSX’s mission and vision of developing the near future to accommodate the needs of society has reminded me about the overarching meaning of robotics research: that is, using robotics to create a better future for society.

Again, I am truly grateful for Dr. Kazutoshi Tanaka and Dr. Masashi Hamaya for their kindness and support. I am also grateful for the researchers of OSX, as well as the other fellow interns that were a part of OSX during my time there, for their kindness and support. I am looking forward to working with them again, if I ever have the privilege of doing so. I would also like to sincerely appreciate the GA Office Center at OSX for being very supportive during my time at OSX and helping me settle down quickly to focus on my internship in Japan.

## References
[1] Arias Guadalupe, Janeth, Dorin Copaci, David Serrano del Cerro, Luis Moreno, and Dolores Blanco. 2021. “Efficiency Analysis of SMA-Based Actuators: Possibilities of Configuration According to the Application” Actuators 10, no. 3: 63. Available: https://doi.org/10.3390/act10030063

[2] Ballester, Carmen, Dorin Copaci, Janeth Arias, Luis Moreno, and Dolores Blanco. 2023. “Hoist-Based Shape Memory Alloy Actuator with Multiple Wires for High-Displacement Applications” Actuators 12, no. 4: 159. Available: https://doi.org/10.3390/act12040159

[3] C. H. Park, K. J. Choi and Y. S. Son, “Shape Memory Alloy-Based Spring Bundle Actuator Controlled by Water Temperature,” in IEEE/ASME Transactions on Mechatronics, vol. 24, no. 4, pp. 1798–1807, Aug. 2019, doi: 10.1109/TMECH.2019.2928881.

[4] Copaci, Dorin, Dolores Blanco, and Luis E. Moreno. 2019. “Flexible Shape-Memory Alloy-Based Actuator: Mechanical Design Optimization According to Application” Actuators 8, no. 3: 63. Available: https://doi.org/10.3390/act8030063

[5] “Electrical Current Actuator”, FURUKAWA TECHNO MATERIAL CO. LTD, https://www.furukawa-ftm.com/tokusyu/english/product/%e9%80%9a%e9%9b%bb%e3%82%a2%e3%82%af%e3%83%81%e3%83%a5%e3%82%a8%e3%83%bc%e3%82%bf/

[6] FLEXINOL® Actuator Spring Technical and Design Data, DYNALLOY inc., Available: https://www.dynalloy.com/tech_data_springs.php

[7] M. Chignoli, D. Kim, E. Stanger-Jones and S. Kim, “The MIT Humanoid Robot: Design, Motion Planning, and Control For Acrobatic Behaviors,” 2020 IEEE-RAS 20th International Conference on Humanoid Robots (Humanoids), Munich, Germany, 2021, pp. 1–8, doi: 10.1109/HUMANOIDS47582.2021.9555782.

[8] S. Gallo, L. Cucu, N. Thevenaz, A. Sengül and H. Bleuler, “Design and control of a novel thermo-tactile multimodal display,” 2014 IEEE Haptics Symposium (HAPTICS), Houston, TX, USA, 2014, pp. 75–81, doi: 10.1109/HAPTICS.2014.6775436.

[9] “Shape Memory Alloys”, Shape Memory and Structures (SMS) Research Group, https://publish.illinois.edu/shape-memory-structures/shape-memory-alloys/

[10] Skyentific, “How bad is Bowden cables for robotics: friction tests (part 2)”, 2021, Online Video: https://www.youtube.com/watch?v=usPjM6weo8o

By：Ken Suzuki
Link：https://medium.com/sinicx/intern-report-development-of-a-faster-shape-memory-alloy-actuator-513ed66dbdbf

Intern Report: Development of a Faster Shape Memory Alloy Actuator


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Mobile gaming is one of the most popular and profitable segments of the gaming industry, with billions of users and revenues worldwide. Mobile gaming is also constantly evolving, thanks to the advances in technology and innovation. In this article, we will explore how three technologies — artificial intelligence (AI), augmented reality (AR), and virtual reality (VR) — are shaping the future of mobile gaming and creating new possibilities for players and developers.

![image](https://miro.medium.com/v2/resize:fit:640/0*fp51dN_2HTsgsb4v)
AI is the technology that enables machines to perform tasks that normally require human intelligence, such as learning, reasoning, and decision making. AI has many applications in mobile gaming, such as:

- AI can create dynamic and diverse game content, such as levels, characters, items, and stories, based on rules or data. This can enhance the replay value and variety of games, as well as reduce the development time and cost.

- AI can tailor the game experience to the preferences and skills of each player, by adjusting the difficulty, feedback, rewards, and recommendations. This can improve the engagement and retention of players, as well as increase their satisfaction and loyalty.

- AI can enable natural and realistic communication and collaboration between players and non-player characters (NPCs), by using natural language processing (NLP), computer vision, and speech synthesis. This can create more immersive and emotional game experiences, as well as foster social connections and communities.

AR is the technology that overlays digital information or objects onto the real world, enhancing the perception of reality. AR has many applications in mobile gaming, such as:

- Location-based gaming: AR can create games that are based on the real-world location of the player, using GPS or other sensors. This can encourage players to explore their surroundings, discover new places, and interact with other players or NPCs in their vicinity.

- Marker-based gaming: AR can create games that are triggered by scanning specific markers or images in the real world, using the camera or other sensors. This can add an element of surprise and fun to games, as well as create opportunities for marketing and advertising.

- Mixed reality gaming: AR can create games that blend the real and virtual worlds seamlessly, using advanced tracking and rendering technologies. This can create a sense of presence and immersion in games, as well as enable new forms of gameplay and interaction.

VR is the technology that creates a fully immersive and interactive simulation of a virtual environment, replacing the perception of reality. VR has many applications in mobile gaming, such as:

- 360-degree gaming: VR can create games that are played in a 360-degree view of a virtual environment, using a VR headset or a smartphone-powered device. This can provide a more realistic and immersive game experience, as well as enable new perspectives and movements.

- Social VR gaming: VR can create games that are played with other players or NPCs in a shared virtual environment, using a VR headset or a smartphone-powered device. This can provide a more social and collaborative game experience, as well as enable new forms of communication and expression.

- Brain-computer interface (BCI) gaming: VR can create games that are controlled by the brain activity of the player, using a BCI device or a smartphone-powered device. This can provide a more intuitive and direct game experience, as well as enable new forms of gameplay and interaction.

The future of mobile gaming is bright and exciting, thanks to the convergence of AI, AR, and VR technologies. These technologies will enable mobile games to become more intelligent, immersive, and interactive, as well as more personalized, social, and creative. Mobile games will not only entertain us but also educate us, inspire us, and connect us with others.

## Read more:

1. https://www.forbes.com/sites/forbestechcouncil/2023/08/25/what-is-the-advent-and-future-of-ai-in-arvr-gaming-technology/
2. The future of the metaverse will be shaped by these 3 technologies | World Economic Forum (weforum.org)
3. Is augmented reality the next level of gaming? — ConsumerLab (ericsson.com)

By：Alexandra Grosu
Link：https://medium.com/@alexandragrosu03/the-future-of-mobile-gaming-ai-ar-and-virtual-reality-ffc55c30515f

The Future of Mobile Gaming: AI, AR, and Virtual Reality


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As technology rapidly advances, the horizon beckons with robots that don’t just function but feel. Thanks to cutting-edge materials like Shape Memory Alloys (SMAs) and Electroactive Polymers (EAPs), robots are taking a step closer to understanding human emotions. But in this evolving landscape, how do we maintain the essence of human relationships and navigate the ethical maze?

## Smart Materials: Robots Getting a ‘Heart’?

- Shape Memory Alloys (SMAs): These unique metals, especially those made of nickel-titanium, can change and revert to shapes, allowing robots to mirror human movements — be it a gentle pat or a playful fist bump.
- Electroactive Polymers (EAPs): These ‘artificial muscles’ change shape with electricity, enabling robots to express emotions, from a celebratory dance to a consoling gesture.
- Dielectric Elastomers: A subtype of EAP, these soft materials are perfect for nuanced robotic expressions, letting them convey emotions like joy or concern.

## The Feeling Robots: How They Sense:

- Ionic Liquids: Special liquids that guide EAPs in changing shapes. By tweaking the electric charge, robots can exhibit varied reactions, much like human moods.
- Thermal Stimuli: Using SMAs, robots can employ temperature changes to express feelings — a warm touch for comfort, a cooler one for reflection.
- Synthetic Neurotransmitters: Essentially ‘mood juices’ for robots, these mirror human brain chemicals. For instance, a synthetic version of serotonin might indicate a robot’s ‘happiness.’

## Ethical Considerations:

As robots become more ‘human-like,’ several ethical questions arise:

- Dependency: Will we become too reliant on robots for emotional support, sidelining human relationships?
- Privacy: With robots understanding our feelings, how do we ensure our emotional data remains private?
- Authenticity: Can a robot’s ‘empathy’ ever match genuine human emotion? And should we allow ourselves to be comforted by artificial feelings?

## Human Relations in a Robotic Era:

While empathetic robots can play supportive roles, nothing can replace the depth and complexity of human relationships. It’s vital to balance our interactions, ensuring we prioritize human bonds. After all, a robot might listen, but only a human can truly understand the nuances of experiences, memories, and shared moments. In a world buzzing with tech, genuine human connections remain our most treasured asset.

## Conclusion:

Empathetic robots, powered by SMAs and EAPs, herald a future where technology and emotions intertwine. But as we stride forward, it’s crucial to tread with awareness, cherishing human relationships and pondering the ethical implications. In shaping a world with feeling robots, we must also nurture our human bonds, ensuring they remain at the heart of our communities.

By：Praveen Rajendran
Link：https://medium.com/@pkrchannels/empathetic-robots-in-sustainable-societies-balancing-tech-and-heart-67b44df8a907

Empathetic Robots in Sustainable Societies: Balancing Tech and Heart


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We are thrilled to announce that our paper has been accepted to the 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023) (acceptance rate of 43.3%)!

Yuki Kadokawa, Masashi Hamaya, Cristian Beltran-Hernandez, and Kazutoshi Tanaka, “Learning Robotic Powder Weighing from Simulation for Laboratory Automation” [paper] [code] [project page]

Kadokawa Yuki is a Ph.D. student at Nara Institute of Science and Technology and was a research intern at OMRON SINIC X. This work has been done during this internship.

This blog briefly introduces our proposed method.
![image](https://miro.medium.com/v2/resize:fit:640/1*J9zSW5vmWpwbk53bouPiQw.gif)

## Background
This study addressed a robotic powder weighing task for laboratory automation. In this task, researchers or workers typically scoop the powder from a bottle and dispense a portion of it into the weighing dish on the scale. This task is essential in various fields, including chemical and pharmaceutical sciences. However, this task is time-consuming as human researchers need to prepare many samples with a milligram-level mass.

This study aims to train the robot to weigh various powders without additional settings and specialized dispensing machines. The robot is expected to perform accurate powder weighing, including the powders that were never weighed, and thus produce adaptive scooping and dumping motion based on the powder states.

Powder weighing also poses several challenges for robotic manipulation. The powder dynamics vary significantly between materials; for example, smaller-size particles tend to be more cohesive, leading to different fluidity. Moreover, for precise weighing of small target masses, the robot must strike a delicate balance between aggressive and conservative actions. For instance, the robot may need to shake the spoon vigorously to dump cohesive powders but not too much to avoid dumping an excessive amount of powder. Hence, learning-based approaches are promising; however, many interactions with real-world environments require significant manual efforts to clean the spread powders.

This study adopted a sim-to-real transfer learning approach to enable the robot to weigh various powders with a small target mass and alleviate the burden of collecting data in a real-world environment. To this end, we employed a Domain Randomization (DR) technique, which can compensate for the sim-to-real gap and adapt to several unseen real environments without additional training. We developed a powder weighing simulator with Isaac Gym and selected the dynamics parameters to be randomized, such as particle friction and gravity.

## Problem setting
To enable a robot to automate this task, we developed the setting, as depicted in Fig. 1. A robot is equipped with a dispensing spoon to scoop and dump powder from a bottle on an electric scale. The objective of the powder weighing task is to adjust the amount of powder in the spoon to a specific target mass ranging between 5 and 15 mg through scooping and dumping actions. The scale measures the weight of the powder in the bottle, and the powder weight on the spoon is indirectly obtained by monitoring the scale measurements of the amount of powder scooped from and dumped into the bottle.
Notably, the setup presented here differs slightly from that of an actual powder weighing scenario. The reason for this deviation is to save time on powder transportation and facilitate easier evaluation in real-world experiments. Nevertheless, our proposed setting can address the fundamental problem of how to adjust the amount of powder through dumping.

![image](https://miro.medium.com/v2/resize:fit:640/format:webp/1*W9N_96KA7GQhgGtnUoVyiw.png)
Fig.1: Our robotic system

## Learning Powder weighing
To train the policy, we used one of the most widely used sample-efficient model-free deep reinforcement learning methods, soft actor-critic (SAC). To consider the time-series powder state, we applied a long short-term memory (LSTM) structure to the actor and critic network. The use of an LSTM-based structure was beneficial in balancing the selection of aggressive and conservative actions. Specifically, if the robot dumped too much powder, the policy undertook more conservative actions in the subsequent timestep. Conversely, if the amount of powder remained constant for several timesteps, the policy selected more aggressive actions.

We designed and implemented a simulator for powder weighing tasks using Isaac Gym. The simulator enables us to adjust the amount of powder on the spoon by inclining and shaking the spoon. Unlike real-world scenarios, the robot arm model and the process of scooping powder from the bottle are excluded. Instead, powder particles spawn on the spoon by randomly changing the particle number. The powder weight on the spoon is calculated by counting the number of particles on the ground.

This study applies a DR technique to obtain the dumping policy to adjust the amount of powder on the spoon in the simulator. Then, the transfer policy is utilized in real-world environments without additional interactions. We selected and randomized eight parameters during train-
ing in the simulator, including the target powder amount, as described in Table I. These parameters were uniformly randomized at every episode.

To consider the variations in the physical properties of the powder, powder friction, mass, and radius were randomized (Table I). Particle number was used for the variation in the initial amount of the powder. Spoon friction is also important to exhibit the various fluidity of the powders. The powder’s movement in the air was mimicked using gravity. The shake speed weight was used to ensure the robustness of the robot’s response to noise and delay in the system. The range of the values of the physical parameters was experimentally determined by observing the powder behavior during the weighing task performed by humans.

![image](https://miro.medium.com/v2/resize:fit:640/format:webp/1*XvbIgCO9w9bKld1WGGwN1g.png)

## Experiments
For the real-world evaluation, we prepared four types of powders that were less harmful and exhibited different fluidity: wheat flour, salt, coal, and rice flour. Table II lists the weighing performances with the four powders. The results show that the accuracy for rice flour and salt was within 0.5 mg, and the performance for coal was approximately 0.5 mg. Wheat flour is the most susceptible to moisture in the air and is prone to the agglomeration phenomenon; therefore, its performance was relatively low. Nevertheless, it achieved an accuracy of approximately 0.5 mg on average.

![image](https://miro.medium.com/v2/resize:fit:640/format:webp/1*LmhRfrpRHrAB3aiQ-yj0tw.png)

![image](https://miro.medium.com/v2/resize:fit:640/1*WN35hgwchiiQTKZpYsxucg.gif)

## Conclusion
This study proposed a powder weighing simulator and utilized it for learning transfer policy with Domain Randomization for sim-to-real. The experimental results showed that the proposed method was applicable to obtain transfer policies, and learning was achievable in a reasonable
time without hard real-robot initialization and maintenance.

If you are interested in our internship, please check our website!

If you are interested in collaborating with us, please send us an e-mail! contact@sinicx.com

## YouTube Video

https://youtu.be/JTltnQrI5sg

By：Masashi Hamaya
Link：https://medium.com/sinicx/learning-powder-weighing-manipulation-4ce2f02fd3e


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We are thrilled to announce that our paper has been accepted to the 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023) (acceptance rate of 43.3%)!

> Joaquín Royo Miquel, Masashi Hamaya, Cristian Beltran-Hernandez, and Kazutoshi Tanaka, “Learning Robotic Assembly by Leveraging Physical Softness and Tactile Sensing” [paper] [project page]

Joaquín Royo Miquel was a research intern at OMRON SINIC X. This work has been done at this internship.

![image](https://miro.medium.com/v2/resize:fit:640/1*lPImsb1b7A8J6QsKdxYvrg.gif)

## Background
Our objective is to enable robots to complete assembly tasks while reducing the engineering effort to eliminate uncertainties, such as sensor calibrations for the precise location of the pieces or the design of jigs to anchor them.

Several soft robots, composed of compliant materials, have demonstrated adaptability in insertion tasks (see our previous blog posts [Hamaya+, 2020] [Tanaka+, 2021]).
However, additional sources of uncertainty, such as variations in the grasping angle, may lead robots to fail the task, for instance, by initiating insertion without properly positioning the part.

This study integrates a tactile sensor into a soft robot toward a more robust assembly. Tactile signal patterns can mark the timing for subtask transitions even in scenarios with imprecise goal positions and grasping poses. In the context of the peg-in-hole task, it is assumed that the presence of a hole can be detected by identifying the distinctive dynamic pattern that the peg generates on the tactile sensor when it encounters the edge of the hole. This is framed as an anomaly detection task.

To accomplish the insertion task, a model-based online learning controller guides the peg’s tip toward the hole. We also incorporate a pose estimation method with a force-torque sensor.

## Overview of Our Proposed Method
Fig. 1 shows the overview of the proposed method composed of (a) a tactile-based hole detection for subtask transition and (b) a search and insertion controller.

The hole detection is used to distinguish the tactile signal patterns generated upon peg-hole contact, thereby triggering the insertion. This study used a vision-based tactile sensor, Digit [Lambeta+, 2020]. We perform signal pre-processing on the tactile sensors to extract frequency features and then apply anomaly detection composed of a Variational AutoEncoder (VAE). The assumption is that the frequency features
undergo significant changes when the peg’s tip fits the hole,
and the anomaly detection method can inform these changes.

The search controller is used to navigate the tip of the peg to the hole. The controller is obtained by a model-based learning approach. We train the forward model of the robot and select the optimal action using a Cross-Entropy Method. Once the cue is received, the search controller is switched for the alignment and insertion controller. Peg tip pose is estimated using tactile and force-torque sensors.

![image](https://miro.medium.com/v2/resize:fit:640/format:webp/1*q8_kjFgN52bi3xdiW2SEsg.jpeg)
Fig.1: Our proposed system

## Experiments
We performed real robot experiments using a soft wrist [Tanaka+, 2020] and a tactile sensor.

The results demonstrate that our method achieves a 100\% success rate in scenarios with less uncertain goal pose ( σ= 2mm) and grasp misalignment (up to 5°) and a 70% success rate in scenarios with uncertain goal pose (σ = 10mm) and grasp misalignment (up to 20°) shown in Fig.2. Moreover, our anomaly detection model can generalize to different peg diameters without additional training.

![image](https://miro.medium.com/v2/resize:fit:640/1*qNZljBhBQqhzDowuZo-7bg.gif)
Fig.2: Robot experiments under grasp pose and hole pose uncertainty

## Future works
We will explore faster task completion and extend our method to adapt to various part shapes.

If you are interested in our internship, please check our website!

If you are interested in collaborating with us, please send us an e-mail! contact@sinicx.com

## YouTube Video

https://youtu.be/jM2kJ1qBok4

By：Masashi Hamaya
Link：https://medium.com/sinicx/learning-soft-robotic-assembly-using-tactile-sensing-20470b22e943

Learning Soft Robotic Assembly using Tactile Sensing


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## NVIDIA’s AI Robotics and Generative AI
Cloud-native APIs and microservices, together with potent generative AI models, are advancing to the edge.

Almost every business is benefiting from the strength of transformer models and huge language models thanks to generative AI. Defect detection, real-time asset tracking, autonomous planning and navigation, human-robot interactions, and other areas that impact on robotics and logistics systems are now included in that reach.

On the NVIDIA Jetson platform for edge AI and robotics, NVIDIA today announced significant expansions to two frameworks: the NVIDIA Isaac ROS robotics framework has gone generally available, and the NVIDIA Metropolis extension on Jetson will follow.

NVIDIA has also developed a Jetson Generative AI Lab for developers to work with the most recent open-source generative AI models in order to hasten the creation and deployment of AI applications at the edge.

NVIDIA AI and the Jetson platform have been adopted by more than 1.2 million developers and over 10,000 clients, including Amazon Web Services, Cisco, John Deere, Medtronic, Pepsi, and Siemens.

Longer development cycles make it difficult for developers to create AI applications for the edge since the AI environment is continuously changing to handle scenarios that are getting more complex. It takes time and expertise to reprogramme robotics and AI systems on the go to adapt to shifting surroundings, production lines, and client automation demands.

To make the creation, deployment, and administration of AI at the edge simpler, generative AI delivers zero-shot learning, which enables a model to recognize things explicitly not seen previously in training.

## AI Landscape Transformation
The simplicity of usage is significantly enhanced by generative AI since it can comprehend human language commands to modify models. These AI models outperform conventional convolutional neural network-based models because they are more adaptable in terms of detecting, segmenting, tracking, searching, and even reprogramming.

ABI Research estimates that by 2033, generative AI will increase manufacturing operations’ global revenue by $10.5 billion.

According to Deepu Talla, vice president of embedded and edge computing at NVIDIA, “Generative AI will significantly accelerate deployments of AI at the edge with better generalization, ease of use, and higher accuracy than previously possible.” The strength of transformer models and generative AI, along with the largest-ever software extension of our Metropolis and Isaac frameworks on Jetson, answers this demand.

## Creating at the Edge of Generative AI
Developers can utilize the Jetson Generative AI Lab’s optimized tools and tutorials to deploy open-source LLMs, diffusion models to create stunning interactive images, vision language models (VLMs), and vision transformers (ViTs), which integrate vision AI and natural language processing to provide a thorough understanding of the scene.

The NVIDIA TAO Toolkit allows developers to construct accurate and effective AI models for edge applications. To optimize vision AI models, such as ViT and vision fundamental models, TAO offers a low-code interface. To construct extremely accurate vision AI models with very minimal data, they can also adapt and improve open-source models like OpenCLIP or fundamental models like NVIDIA NV-DINOv2. Additionally, a new transformer-based defect inspection model called VisualChangeNet is now part of TAO.

## Using the Isaac and New Metropolis Frameworks
Enterprises may more easily and affordably adopt cutting-edge, vision AI-enabled solutions to solve pressing operational efficiency and safety issues thanks to NVIDIA Metropolis. For developers to create complicated vision-based apps fast, the platform offers a collection of strong APIs and microservices.

NVIDIA Metropolis developer tools are being used by more than 1,000 businesses, including BMW Group, Pepsico, Kroger, Tyson Foods, Infosys, and Siemens, to tackle operational, Internet of Things, and sensor processing concerns using vision AI, and the adoption rate is accelerating. By individuals wishing to develop vision AI applications, the tools have already been downloaded more than one million times.

By the end of the year, an enhanced set of Metropolis APIs and microservices on NVIDIA Jetson will be accessible, enabling developers to design and deploy scaled vision AI applications more rapidly.

The NVIDIA Isaac platform is used by hundreds of clients to create high-performance robotics solutions for a variety of industries, including agricultural, warehouse automation, last-mile delivery, and service robots.

With updated versions of the Isaac ROS and Isaac Sim software, NVIDIA made significant advancements to perception and simulation capabilities at ROSCon 2023. Isaac ROS, which is based on the widely used open-source Robot Operating System (ROS), adds awareness to automation by providing moving objects eyes and hearing. Robotics developers may quickly create robotics systems specifically suited for a wide range of applications by utilizing the capability of GPU-accelerated GEMs, such as visual odometry, depth perception, 3D scene reconstruction, localization, and planning.

Since the most recent Isaac ROS 2.0 version is now generally accessible, developers may now use Jetson to design and commercialize high-performance robotics products.

According to Geoff Biggs, CTO of the Open Source Robotics Foundation, “ROS continues to grow and evolve to provide open-source software for the entire robotics community.” This release’s introduction of NVIDIA’s new prebuilt ROS 2 packages, which make ROS 2 easily accessible to the sizable NVIDIA Jetson development community, will hasten this expansion.

## Providing fresh examples of AI workflows
A production-ready AI solution must be created by optimizing the creation and training of AI models that are suited to certain use cases, putting strong security measures on the platform, coordinating the application, managing fleets, and more.

The Metropolis and Isaac frameworks are the foundation of NVIDIA’s curated collection of AI reference workflows, which allow developers to rapidly adopt the complete workflow or only integrate certain components, resulting in significant savings in both development time and cost. Network video recording, automatic optical inspection, and autonomous mobile robot are the three different AI processes.

“NVIDIA Jetson, with its broad and diverse user base and partner ecosystem, has helped drive a revolution in robotics and AI at the edge,” said Jim McGregor, principal analyst at Tirias Research. “As application needs get more complicated, we need to make a fundamental switch to platforms that make building edge deployments easier and faster. Developers now have access to new multi-sensor models and generative AI capabilities thanks to NVIDIA’s major software expansion.

## There will be more to come
When developing cutting-edge AI solutions, every developer needs a set of essential capabilities, which NVIDIA has unveiled as a collection of system services. These services will make workflow integration simpler and spare developers the laborious process of creating them from scratch.

By enabling AI developers to stay on the bleeding edge of computing without the requirement for a full Jetson Linux upgrade, the new NVIDIA JetPack 6, which is anticipated to be ready by the end of the year, will significantly shorten development schedules and free them from Jetson Linux dependencies. The joint efforts of JetPack 6 and Linux distribution partners will also be used to broaden the range of Linux-based distribution options, including Canonical’s Optimized and Certified Ubuntu, Wind River Linux, Concurrent Real-Time Redhawk Linux, and numerous Yocto-based distributions.

## Platform Expansion Benefits the Partner Ecosystem
A comprehensive range of support is offered by the Jetson partner ecosystem, including hardware, AI software, application design services, sensors, networking, and developer tools. These NVIDIA Partner Network innovators are essential in delivering the components and supporting systems for many commercially available devices.

By：Govindhtech
Link：https://medium.com/@govindhtech2023/nvidias-robotics-expansion-exploits-ai-10b5036fe517


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Useful work by the end of 2023 is the goal

We’re thrilled to introduce you to Figure, a humanoid robot that’s making waves in the robotics industry. Within just a year of its inception, Figure has showcased its dynamic bipedal walking capabilities, setting it apart from its competitors. This isn’t just any robot; it’s a glimpse into the future of the workforce.

## A Pragmatic Approach to Robotics
At Figure, we believe in practicality. While many are fascinated by robots that can perform acrobatics, our focus is on creating a robot that can integrate seamlessly into work environments. Our primary goal? To design a robot that can start with simple tasks, such as moving objects in a warehouse, and gradually take on more complex responsibilities.

Our team, comprising over 60 experts from renowned companies like Boston Dynamics, Google Deepmind, Tesla, and Apple, is dedicated to revolutionizing the general-purpose robot worker space. And with the rapid progress we’ve made, we’re well on our way.

![image](https://miro.medium.com/v2/resize:fit:640/format:webp/1*6bFNlaNXvHuzoojMT_WIKA.png)
General-purpose robots are designed in humanoid form so they can directly take over physical tasks performed by humans, using the same tools and access methods

## Dynamic Bipedal Walking: A Game-Changer
One of the standout features of Figure is its dynamic bipedal walking. Achieved in less than 12 months, this feature allows Figure to move swiftly and smoothly, outpacing many of its counterparts. The smooth gait of Figure is a testament to our team’s dedication and expertise.

But that’s not all. Figure is also adept at various tasks such as picking up objects, navigating its surroundings, and more — all autonomously. Our aim is for Figure to be fully operational and contributing to real-world tasks by the end of the year.

![image](https://miro.medium.com/v2/resize:fit:640/format:webp/1*CacrIhRzS7ZLRh56L8hJhw.png)
The Figure 01 prototype’s hip and knee joints each put out over 200 Nm of electric torque

## Torque-Controlled Walking: The Modern Approach
When it comes to walking, Figure uses torque-controlled walking, a modern approach that allows for dynamic interaction with the environment. Unlike traditional position and velocity control, torque control measures the forces exerted on the world, enabling the robot to react instantaneously. This method is akin to how humans perceive and balance, making Figure’s movements more natural and adaptable.

![image](https://miro.medium.com/v2/resize:fit:640/format:webp/1*G3qVjToXNV6wjAVYCW-GPA.png)
Bipedal walking within 12 months is quite an achievement — and a record, Adcock believes

## Advanced Manipulation Capabilities
Figure’s hands, designed in-house with silicone soft-grip fingertips, are capable of both single-handed and bimanual manipulation. Whether it’s picking up boxes in a warehouse or handling delicate consumer items, Figure handles it all with precision. And the best part? Most of these tasks are performed autonomously, showcasing the robot’s advanced AI capabilities.

https://youtu.be/jACJruCzUzY

## Communication: Bridging the Gap Between Humans and Robots
One of the unique features of Figure is its face-level screen, designed to communicate the robot’s intentions. Whether it’s displaying its current task or what it plans to do next, this screen ensures that humans can easily understand and interact with Figure. And yes, we’re even exploring the possibility of Figure communicating through speech!

![image](https://miro.medium.com/v2/resize:fit:640/format:webp/1*opyXQvfmluzWnWNkxVcLfg.png)
Looking into the shoulder joint

## The Road Ahead
Our journey with Figure has been exhilarating, but we’re just getting started. Our next steps involve integrating all autonomous systems into the robot, enhancing its robustness, and ensuring it can perform end-to-end tasks seamlessly. With five units already assembled, we’re gearing up for extensive testing and improvements.

![image](https://miro.medium.com/v2/resize:fit:640/format:webp/1*3-AOJOc7xkejJv6joBl7SQ.png)
The face-level screen will be used to communicate the robot’s current and future intentions

## In Conclusion
Figure represents our vision for the future — a world where robots and humans work side by side, complementing each other’s strengths. We’re not just building a robot; we’re crafting the future of the workforce. And with Figure leading the way, the future looks promising.

By：AI Agenda
Link：https://medium.com/@ai.agenda/the-novak-element-figure-robotics-the-future-of-humanoid-workforce-fba47f0fe0bb

The Novak Element: Figure Robotics- The Future of Humanoid Workforce


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In the realm of technological advancements, artificial intelligence (AI) and robotics are among the most transformative forces reshaping our world. While they bring about the promise of streamlined operations and unmatched efficiency, the economic and societal challenges they pose are just as profound. One of the most significant concerns arising is the potential economic disparity resulting from automation and the need to ponder upon caps for individual earnings. Let’s delve deeper into this evolving paradigm.

## 1. The Promise of AI and Robotics

The impact of AI and robotics on various industries cannot be understated. From manufacturing to healthcare, logistics to finance, these technologies promise:

- Efficiency: Machines can work around the clock, free from the bounds of human fatigue.
- Precision: Robots excel in tasks requiring meticulous detail, minimizing human error.
- Scalability: As operations grow, AI-driven systems can be scaled without the extensive training processes required for human workers.

## 2. The Economic Ripples of Automation

The economic shifts brought about by these technologies are multifaceted:

- Profitability: As operations become more streamlined, industries are witnessing an increase in profits, particularly those that rely heavily on manual labor.
- Job Displacement: Historically, every industrial revolution led to the displacement of certain jobs. With AI and robotics, jobs primarily involving repetitive tasks are the most vulnerable.

## 3. The Erosion of Tax Revenues

With the transition to AI-driven operations, governments are grappling with an unexpected challenge:

- Declining Income Taxes: As human roles diminish in certain sectors, so does the revenue from personal income taxes.
- Taxation Void: Machines, unlike humans, don’t earn salaries and therefore don’t pay income taxes. This situation creates an unanticipated shortfall in government coffers.

## 4. Economic Disparities and the Wealth Chasm

One of the most pressing concerns is the widening gap between different societal segments:

- Accumulation of Wealth: As profits soar in industries heavily reliant on AI and robotics, the wealth tends to accumulate in the hands of a few.
- Stagnating Wages: For many sectors not yet influenced by AI, wages remain stagnant, leading to reduced purchasing power and potential socioeconomic unrest.

## 5. Societal Implications of Disparity

The repercussions of such economic imbalances can be vast:

- Societal Unrest: History has shown that significant economic disparities often result in societal tensions and unrest.
- Reduced Market Activity: If a large segment of the population has reduced earning power, their ability to participate in the economy diminishes, leading to potential economic downturns.

## 6. The Need for Earnings Caps

To mitigate the risks of such disparities, the concept of earnings caps is gaining traction:

- Equitable Distribution: By placing a limit on how much an individual can earn, societies could promote a more balanced distribution of wealth.
- Reinvestment in Society: Excess earnings, beyond the cap, could be channeled back into societal infrastructure, education, and welfare, thus benefiting the community at large.

## 7. Potential Mechanisms for Balance

Beyond earnings caps, there are other avenues to consider:

- Robot Tax: A tax on AI-driven operations or robots could help bridge the taxation void and ensure continued public funding.
- Universal Basic Income (UBI): As a counter to job losses from automation, a UBI could provide a safety net, ensuring every citizen has basic financial support.
- Re-skilling Initiatives: Investing in education and training programs that help workers transition to roles that are less vulnerable to automation.

## 8. The Roadblocks to Implementing Caps

While the idea of earnings caps might seem like a panacea, its implementation is fraught with challenges:

- Defining Limits: Establishing what constitutes an ‘excessive’ earning is subjective and can vary across cultures and economies.
- Global Consistency: In an interconnected global economy, creating a universally accepted cap could be challenging.
- Economic Incentives: Caps might deter innovation and entrepreneurship if individuals feel their efforts won’t be adequately rewarded.

## 9. The Ethical Dimension

At the heart of this debate lies a profound ethical question:

- Rights and Limits: What rights do individuals have to the fruits of their labor and innovation? And how do societies balance individual rights with collective well-being?

## 10. Concentration of Wealth and Power: A Democratic Dilemma

### Introduction to the Issue:

In tandem with the rise of AI and robotics, there has been a notable shift in the distribution of wealth and power. While technological advancements promise efficiency and growth, they also inadvertently lead to the concentration of resources in the hands of a select few. This poses significant questions about the democratic ideals of equality, participation, and representation.

### Historical Perspective:

Throughout history, significant wealth accumulation often led to socio-political imbalances. Whether it was the industrial magnates of the past or today’s tech moguls, an enormous concentration of resources typically signaled challenges to the democratic fabric.

### Implications for Democracy:

- Reduced Representation: When power and resources are confined to a few, decisions may prioritize their interests over the collective good. This could lead to policies that benefit the elite while sidelining the concerns of the majority.
- Barrier to Participation: In democratic societies, one of the foundational tenets is the participation of all in civic duties and decision-making processes. However, if vast portions of resources are controlled by few, it may lead to a disillusioned majority who feel their voice or vote holds less weight.
- Inequality: At the heart of democracy is the principle of equality. However, significant wealth disparities can lead to inequalities in access to opportunities, education, and even basic amenities.

### The AI and Robotics Angle:

AI and robotics, by virtue of their scalability and efficiency, offer tremendous profitability. Without adequate checks and balances, this profitability can be disproportionately captured by those who control these technologies, further exacerbating wealth concentration.

### Potential Solutions:

- Regulation and Oversight: Governments can play a pivotal role by ensuring that tech industries operate within frameworks that prioritize societal good over mere profitability.
- Redistribution Mechanisms: Policies such as progressive taxation, or even the proposed robot tax, can help in more equitable wealth distribution.
- Promoting Grassroots Innovations: Encouraging and financially supporting tech initiatives at the grassroots level can ensure that benefits of AI and robotics seep down to broader sections of society.

## Conclusion

The intersection of AI, robotics, and economic structures is shaping up to be one of the most significant challenges of our times. While technological advancements promise a new era of efficiency, it is imperative to address the accompanying economic and societal challenges head-on. Implementing mechanisms like earnings caps, though complex, might be a step towards a more harmonious economic landscape. It is a testament to our times that as we engineer sophisticated machines, we are also tasked with re-engineering the very fabric of our societal structures. The balance we strike will define the harmony and prosperity of future generations.

By：Praveen Rajendran
Link：https://medium.com/@pkrchannels/ai-robots-and-the-balance-of-economic-prosperity-disparities-taxation-and-societal-harmony-1a8a2bb0c0bb

AI, Robots, and the Balance of Economic Prosperity: Disparities, Taxation, and Societal Harmony


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Hey there, tech-savvy explorers! Today, let’s embark on a thrilling journey into the realm of AI chatbots. These digital companions have been making quite a splash in our everyday lives, but like any adventure, they come with their own set of positives and negatives. So, grab your virtual backpack, and let’s delve into the captivating world of chatbots.

## The Pros: High Fives to AI Chatbots! 🙌
Let’s kick things off with the positives, shall we? There’s a lot to love about AI chatbots, and they’ve certainly earned their place in our digital landscape.

### 1. Available 24/7: A Friend Who Never Sleeps

Perhaps the most significant advantage of AI chatbots is their unwavering availability. They don’t take breaks, holidays, or power naps. When you’re burning the midnight oil with a question or a concern, your trusty chatbot friend is right there to lend a hand.

### 2. Lightning-Fast Responses: No More Elevator Music

Say goodbye to those long, frustrating hold times on customer service calls. AI chatbots are lightning quick when it comes to answering inquiries. No more elevator music or boring hold tunes — your answer is just a chat away!

### 3. Multitasking Marvels: Handling Numerous Queries Simultaneously

Ever seen a human customer service agent juggle multiple conversations at once? Well, chatbots are pros at it! They can handle dozens of inquiries simultaneously, making sure everyone gets their questions answered in record time.

### 4. Data Sorting Wizards: The Digital Librarians

Getting buried under an avalanche of data and documents? Chatbots can help you navigate this information overload. They’re like digital librarians who quickly fetch the right documents and data, saving you from an epic search expedition.

### 5. Onboarding and Training: A Teacher’s Best Friend

For businesses, chatbots are turning into fantastic onboarding and training assistants. They can guide new employees, answer common questions, and even facilitate training modules, making the learning process smoother and more efficient.

### 6. Global Language Skills: Inclusivity at Its Finest

One of the coolest features of chatbots is their multilingual communication ability. They break down language barriers and ensure workplaces are more inclusive and accessible to a global workforce.

![image](https://miro.medium.com/v2/resize:fit:640/format:webp/1*GblMNmUWhFSu_CdGbOyxgg.jpeg)
AI Chatbot

## The Cons: The Flaws in the Code 🤖
Now that we’ve celebrated the positives, let’s switch gears and explore the less-than-perfect aspects of chatbots.

### 1. Limited Understanding: Frustration Alert

Chatbots, while impressive, still have their limitations. They may misinterpret complex queries or struggle with context. This can lead to frustrating interactions, especially when you need a nuanced response.

### 2. Lack of Empathy: No Hugs from Chatbots

As clever as chatbots are, they can’t truly understand or express empathy. When dealing with emotionally charged situations, a chatbot’s robotic responses might feel a bit impersonal or cold.

### 3. Privacy Concerns: Data Dilemmas

Using chatbots often involves sharing personal or sensitive information. While many organizations take data security seriously, there’s always a risk of data breaches or misuse, which can lead to privacy concerns.

### 4. Misguided Information: Garbage In, Garbage Out

Garbage in, garbage out — this old computer science adage is especially relevant when it comes to chatbots. If the data they’re trained on is biased or inaccurate, chatbots can inadvertently spread misinformation.

### 5. Loss of Jobs: The Automation Dilemma

There’s no denying that chatbots are automating tasks that were once done by humans. While this boosts efficiency, it can also lead to job displacement in some industries, causing economic concerns.

### 6. Technical Glitches: The Digital Gremlins

Like any technology, chatbots can suffer from technical glitches. They might go offline unexpectedly or provide incorrect information due to software bugs. When this happens, it can leave users in the lurch.

## The Fascinating Reality: The Human + AI Synergy 🌟

In the world of AI chatbots, the reality is that it’s not all good, and it’s not all bad. The true magic happens when humans and AI work in harmony. Here’s how:

### 1. Enhanced Efficiency: A Power Duo

When humans and chatbots team up, they become a dynamic duo of productivity. The chatbot handles routine, time-consuming tasks, while humans focus on complex, creative, and emotionally intelligent tasks that AI can’t replicate.

### 2. Continuous Improvement: Humans as Chatbot Mentors

Chatbots learn and adapt based on their interactions with humans. As users, we play a role in their evolution. Our feedback and corrections help train them to provide better, more accurate responses.

### 3. Crisis Response: Chatbots + Human Support

In times of crisis or high emotion, humans excel at providing empathy and understanding. Chatbots, on the other hand, can swiftly disseminate crucial information and assist with logistics. Together, they create a strong support system.

## Closing Thoughts: A Work in Progress

AI chatbots are a work in progress, a digital experiment that’s changing the way we interact with technology. They have their pros and cons, but the key to a harmonious relationship lies in understanding these nuances and leveraging their strengths while mitigating their weaknesses.

The future holds immense promise as AI chatbots continue to evolve, offering more personalized and empathetic interactions. They may not replace the human touch, but they’ll definitely continue to enhance our digital experiences and redefine how we work and communicate. So, let’s embrace this fascinating journey into the world of chatbots, knowing that, like any adventure, it’s full of surprises, challenges, and opportunities for growth. 🤖✨

By：Daniel Ferguson
Link：https://medium.com/@danielferguson283/chatbots-unveiled-the-good-the-bad-and-the-truly-fascinating-5abfc3689596

Chatbots Unveiled: The Good, The Bad, and the Truly Fascinating!


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## Introduction

In the realm of technology, few subjects have captured the imagination and interest of the world as profoundly as Artificial Intelligence (AI). From its conceptualization in the mid-20th century, AI has transitioned from the realm of science fiction to an integral part of our daily lives. It’s in our smartphones, powers our virtual assistants, and even dictates how our online searches yield results. Over the decades, AI has not been a static field; it has evolved, branched, and specialized. Each wave of innovation has been marked by a deeper understanding of algorithms, more powerful computational capabilities, and a closer alignment to the way humans think and learn.

Just when it seemed like the wonders of neural networks and deep learning were reaching their zenith, a new player entered the arena, promising to redefine the boundaries of AI once again: Deep Reinforcement Learning (DRL). Merging the decision-making prowess of reinforcement learning with the unparalleled pattern-recognition capabilities of deep learning, DRL stands as a beacon of the next phase of AI evolution. It promises not just to recognize and classify, but to act, learn from interactions, and continuously improve — a true embodiment of an intelligent agent.

In the subsequent sections, we will dive into the intricacies of DRL, explore its potential, and understand why it’s being heralded as the next big leap in the ever-fascinating journey of AI.

## What is Deep Reinforcement Learning?

At its core, Reinforcement Learning (RL) is a type of machine learning where an agent learns to make decisions by interacting with an environment. In RL, the agent takes actions based on its current state, and for each action, the environment provides feedback in the form of rewards or penalties. The objective of the agent is to maximize its cumulative reward over time.

Formally, RL can be described using the framework of Markov Decision Processes (MDPs). An MDP consists of:

- States (S): A set of states that represent different situations or configurations in the environment.
- Actions (A): A set of possible actions the agent can take.
- Rewards (R): A function that gives a scalar reward for each state-action pair.
- Transitions (T): A probabilistic function describing the likelihood of transitioning from one state to another given an action.
- Discount Factor (γ): A factor that models the agent’s consideration for future rewards. A value close to 0 makes the agent myopic, while a value close to 1 makes it far-sighted.

The agent’s strategy, known as a policy (π), is a mapping from states to actions. The goal in RL is to find an optimal policy that maximizes the expected cumulative reward.

Deep Learning (DL) leverages neural networks with many layers (deep neural networks) to approximate complex functions. These networks are capable of extracting intricate patterns and representations from raw data, be it images, text, or sound. In the context of RL, deep learning can be used to approximate the value functions or policies, especially when the state or action spaces are large or continuous.

Deep Reinforcement Learning (DRL) marries the decision-making framework of RL with the function approximation capabilities of deep learning. In scenarios where the state or action space is too vast or the environment is too complex for traditional RL techniques, deep neural networks come to the rescue. For example:

- Q-learning with Deep Q-Networks (DQN): Instead of maintaining a table for Q-values, a neural network is trained to approximate the Q-function. This allows for generalization across states and actions.
- Policy Gradient Methods with Neural Policies: The policy itself is parameterized by a neural network, and optimization techniques are used to directly improve the policy.
- Actor-Critic Architectures: Combine the strengths of value-based and policy-based methods, using neural networks for both the policy (actor) and value estimation (critic).

By leveraging deep learning, DRL can handle environments with high-dimensional input spaces, like images or unstructured data, making it suitable for a wide range of complex real-world applications.

## Why is DRL Important?

One of the most compelling advantages of Deep Reinforcement Learning (DRL) is its ability to scale to problems with vast state and action spaces. Traditional reinforcement learning techniques, which often rely on tabular methods, become infeasible when faced with environments that have a large number of states or actions. DRL, with its deep neural networks, can approximate value functions or policies for these massive spaces, allowing it to tackle problems that were previously out of reach.

Deep neural networks, with their layered architecture and non-linear activation functions, excel at identifying patterns and abstracting features from raw data. This inherent ability to generalize means that a DRL agent trained in one environment can often adapt to similar but slightly different environments with minimal additional training. This property is invaluable in real-world scenarios where exact replication of training conditions is challenging.

DRL’s ability to learn directly from raw data without the need for handcrafted features sets it apart. Whether it’s pixel data from a video game, raw sensor data from a robot, or market fluctuations in financial data, DRL can process, interpret, and act upon it. This end-to-end learning streamlines the process of deploying reinforcement learning in diverse settings, removing the bottleneck of feature engineering.

Unlike traditional machine learning models that are often static post-training, DRL agents continuously interact with their environment, learning and adapting their strategies over time. This dynamic learning process ensures that DRL agents remain relevant and effective even as the environment or the underlying data distributions change.

Many AI challenges, from game playing to real-world robotics, don’t fit neatly into supervised or unsupervised learning paradigms. DRL, with its focus on decision-making through interaction, offers a framework that’s inherently suited to these challenges. By integrating decision-making (reinforcement learning) with perception (deep learning), DRL brings us closer to building AI systems that can perceive, reason, and act in complex environments.

In essence, the importance of Deep Reinforcement Learning lies in its versatility, adaptability, and capacity to handle intricate problems that have been challenging for traditional AI techniques. It represents a significant step towards creating AI agents that can operate autonomously in diverse and dynamic environments.

![image](https://miro.medium.com/v2/resize:fit:640/0*H1pgyu6rOZX7YIo8)
## Real-world Applications of DRL

1. Gaming: DRL has achieved superhuman performance in games like Go (AlphaGo) and various Atari titles, showcasing its decision-making prowess.

2. Robotics: Robots use DRL to learn tasks like object manipulation, walking, and navigation, allowing them to adapt to dynamic environments.

3. Healthcare: DRL aids in personalized treatment recommendations, optimizing patient care paths, and even drug discovery by navigating complex biochemical interactions.

4. Finance: In the financial sector, DRL assists in portfolio optimization, risk management, and the development of adaptive trading strategies.

5. Transportation: DRL plays a role in the development of autonomous vehicles, optimizing traffic signal timings, and route planning to enhance transportation efficiency.

![image](https://miro.medium.com/v2/resize:fit:1400/0*6z96psps-yddoK-S)

## Challenges and Limitations

1. Data Efficiency: DRL typically needs extensive data to learn effectively, which can be resource-intensive.

2. Exploration vs. Exploitation: Balancing the act of exploring new strategies versus exploiting known ones is a core challenge.

3. Stability and Convergence: DRL algorithms can sometimes be unstable and may not always converge to an optimal policy.

4. Transfer Learning: Transferring knowledge from one domain to another effectively remains a hurdle.

5. Safety Concerns: Ensuring that DRL agents don’t make hazardous decisions, especially in real-world critical applications.

## Future of DRL

1. Improved Algorithms: Future DRL will likely see the development of more robust and stable algorithms, leveraging techniques such as target network updates, double Q-learning, and prioritized experience replay.

2. Hybrid Models: Combining model-based and model-free approaches can lead to better sample efficiency and improved stability in learning.

3. Meta-Learning: Training agents to learn how to learn can facilitate faster adaptation to new tasks with minimal data.

4. Interpretable Agents: Incorporating explainability into DRL will be crucial. Techniques like saliency maps, attention mechanisms, and layer-wise relevance propagation can make agents’ decisions more transparent.

5. Regularization and Generalization: Adopting advanced regularization techniques will ensure better generalization of agents across diverse environments.

6. Safety Constraints: Integrating safety mechanisms, such as Constrained Policy Optimization, will be pivotal for deploying DRL in critical applications.

## Conclusion

Deep Reinforcement Learning (DRL) stands at the forefront of the AI revolution, merging the power of deep learning’s pattern recognition with the dynamic decision-making capabilities of reinforcement learning. Its achievements in diverse domains, from gaming to healthcare, are testament to its transformative potential. Yet, like any evolving technology, it comes with its set of challenges. As researchers and practitioners continue to push the boundaries of DRL, refining its algorithms and addressing its limitations, we are on the cusp of realizing an era where AI agents can autonomously and intelligently interact with complex environments. The journey of DRL, while already impressive, is just beginning, and its future promises even more groundbreaking advancements in the realm of artificial intelligence.

## Further Reading and Resources

Li, Y. (2017). Deep reinforcement learning: An overview. arXiv preprint arXiv:1701.07274.

Sewak, M. (2019). Deep reinforcement learning. Singapore: Springer Singapore.

By：Eduardo C. Garrido Merchán
Link：https://medium.com/@eduardogarrido90/why-deep-reinforcement-learning-is-going-to-be-the-next-big-deal-in-ai-2e796bdf47d2

Why deep reinforcement learning is going to be the next big deal in AI

Why Deep Reinforcement Learning is Going to be the Next Big Deal in AI: A Comprehensive Exploration of DRL’s Potential, Challenges, and Future Prospects


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Artificial Intelligence (AI) stands at the forefront of technological advancements, representing the emulation of human intelligence in machines. This field of computer science aims to equip machines with the ability to think, learn, and perform tasks that traditionally require human intelligence. From machine learning algorithms to natural language processing and robotics, AI encompasses a broad spectrum of technologies that have far-reaching implications for various industries and everyday life.

## Foundations of AI:
### 1. Machine Learning:
At the heart of AI is machine learning, a subset that focuses on developing algorithms enabling machines to improve their performance on a task by learning from data. Whether it’s recognizing patterns in data, making predictions, or identifying anomalies, machine learning plays a pivotal role in enhancing the capabilities of AI systems.

### 2. Neural Networks:

Inspired by the human brain, neural networks are structures of interconnected nodes (neurons) organized in layers. Deep learning, an advanced form of machine learning, involves deep neural networks with multiple layers, allowing systems to learn intricate patterns and representations.

### 3. Natural Language Processing (NLP):

NLP facilitates communication between computers and human languages. From speech recognition and language translation to sentiment analysis, NLP enables machines to comprehend, interpret, and generate human-like language.

## Application Domains:
###  1. Computer Vision:
Computer vision empowers machines to interpret and make decisions based on visual data, such as images and videos. Applications range from facial recognition and object detection to the development of autonomous vehicles.

### 2. Expert Systems:

Expert systems are designed to replicate the decision-making abilities of human experts in specific domains. They utilize knowledge bases and rule-based systems to provide recommendations or solutions.

### 3. Robotics:

AI plays a critical role in robotics, allowing robots to perceive their environment, make decisions, and execute tasks autonomously. This has implications for industries ranging from manufacturing to healthcare.

## Types of AI:
###  1. Narrow or Weak AI:
Narrow AI is designed for specific tasks, with capabilities limited to a particular domain. Examples include virtual personal assistants, image recognition software, and speech recognition systems.

### 2. General or Strong AI:

Strong AI represents a more advanced form with the ability to understand, learn, and apply knowledge across diverse tasks, akin to human intelligence. Achieving strong AI remains a theoretical goal with profound implications.

## Evolving Technologies and Ethical Considerations:
As AI technologies continue to advance, ethical considerations become paramount. Responsible development practices, transparency, and safeguards against biases are crucial to ensuring that AI benefits society without compromising privacy or perpetuating discrimination.

## Conclusion:
Artificial Intelligence is a transformative force reshaping the landscape of technology and human-machine interactions. From enhancing efficiency in industries to creating innovative solutions, AI’s impact is profound and multifaceted. As researchers and developers continue to push the boundaries of what’s possible, the responsible and ethical deployment of AI remains a key focus, ensuring that these powerful technologies contribute positively to the betterment of society.

By：Antonio Pappaterra
Link：https://medium.com/@pappaterratony/understanding-artificial-intelligence-a-comprehensive-overview-73bc0ab917cf

Understanding Artificial Intelligence: A Comprehensive Overview