Tag Archives: manufacturing

#439004 Video Friday: A Walking, Wheeling ...

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We’ll also be posting a weekly calendar of upcoming robotics events for the next few months; here's what we have so far (send us your events!):

RoboSoft 2021 – April 12-16, 2021 – [Online Conference]
ICRA 2021 – May 30-5, 2021 – Xi'an, China
Let us know if you have suggestions for next week, and enjoy today's videos.

This is a pretty terrible video, I think because it was harvested from WeChat, which is where Tencent decided to premiere its new quadruped robot.

Not bad, right? Its name is Max, it has a top speed of 25 kph thanks to its elbow wheels, and we know almost nothing else about it.

[ Tencent ]

Thanks Fan!

Can't bring yourself to mask-shame others? Build a robot to do it for you instead!

[ GitHub ]

Researchers at Georgia Tech have recently developed an entirely soft, long-stroke electromagnetic actuator using liquid metal, compliant magnetic composites, and silicone polymers. The robot was inspired by the motion of the Xenia coral, which pulses its polyps to circulate oxygen under water to promote photosynthesis.

In this work, power applied to soft coils generates an electromagnetic field, which causes the internal compliant magnet to move upward. This forces the squishy silicone linkages to convert linear to the rotational motion with an arclength of up to 42 mm with a bandwidth up to 30 Hz. This highly deformable, fast, and long-stroke actuator topology can be utilized for a variety of applications from biomimicry to fully-soft grasping to wearables applications.

[ Paper ] via [ Georgia Tech ]

Thanks Noah!

Jueying Mini Lite may look a little like a Boston Dynamics Spot, but according to DeepRobotics, its coloring is based on Bruce Lee's Kung Fu clothes.

[ DeepRobotics ]

Henrique writes, “I would like to share with you the supplementary video of our recent work accepted to ICRA 2021. The video features a quadruped and a full-size humanoid performing dynamic jumps, after a brief animated intro of what direct transcription is. Me and my colleagues have put a lot of hard work into this, and I am very proud of the results.”

Making big robots jump is definitely something to be proud of!

[ SLMC Edinburgh ]

Thanks Henrique!

The finals of the Powered Exoskeleton Race for Cybathlon Global 2020.

[ Cybathlon ]

Thanks Fan!

It's nice that every once in a while, the world can get excited about science and robots.

[ NASA ]

Playing the Imperial March over footage of an army of black quadrupeds may not be sending quite the right message.

[ Unitree ]

Kod*lab PhD students Abriana Stewart-Height, Diego Caporale and Wei-Hsi Chen, with former Kod*lab student Garrett Wenger were on set in the summer of 2019 to operate RHex for the filming of Lapsis, a first feature film by director and screenwriter Noah Hutton.

[ Kod*lab ]

In class 2.008, Design and Manufacturing II, mechanical engineering students at MIT learn the fundamental principles of manufacturing at scale by designing and producing their own yo-yos. Instructors stress the importance of sustainable practices in the global supply chain.

[ MIT ]

A short history of robotics, from ABB.

[ ABB ]

In this paper, we propose a whole-body planning framework that unifies dynamic locomotion and manipulation tasks by formulating a single multi-contact optimal control problem. This is demonstrated in a set of real hardware experiments done in free-motion, such as base or end-effector pose tracking, and while pushing/pulling a heavy resistive door. Robustness against model mismatches and external disturbances is also verified during these test cases.

[ Paper ]

This paper presents PANTHER, a real-time perception-aware (PA) trajectory planner in dynamic environments. PANTHER plans trajectories that avoid dynamic obstacles while also keeping them in the sensor field of view (FOV) and minimizing the blur to aid in object tracking.

Extensive hardware experiments in unknown dynamic environments with all the computation running onboard are presented, with velocities of up to 5.8 m/s, and with relative velocities (with respect to the obstacles) of up to 6.3 m/s. The only sensors used are an IMU, a forward-facing depth camera, and a downward-facing monocular camera.

[ MIT ]

With our SaaS solution, we enable robots to inspect industrial facilities. One of the robots our software supports, is the Boston Dynamics Spot robot. In this video we demonstrate how autonomous industrial inspection with the Boston Dynamics Spot Robot is performed with our teach and repeat solution.

[ Energy Robotics ]

In this week’s episode of Tech on Deck, learn about our first technology demonstration sent to Station: The Robotic Refueling Mission. This tech demo helped us develop the tools and techniques needed to robotically refuel a satellite in space, an important capability for space exploration.

[ NASA ]

At Covariant we are committed to research and development that will bring AI Robotics to the real world. As a part of this, we believe it's important to educate individuals on how these exciting innovations will make a positive, fundamental and global impact for years to come. In this presentation, our co-founder Pieter Abbeel breaks down his thoughts on the current state of play for AI robotics.

[ Covariant ]

How do you fly a helicopter on Mars? It takes Ingenuity and Perseverance. During this technology demo, Farah Alibay and Tim Canham will get into the details of how these craft will manage this incredible task.

[ NASA ]

Complex real-world environments continue to present significant challenges for fielding robotic teams, which often face expansive spatial scales, difficult and dynamic terrain, degraded environmental conditions, and severe communication constraints. Breakthrough technologies call for integrated solutions across autonomy, perception, networking, mobility, and human teaming thrusts. As such, the DARPA OFFSET program and the DARPA Subterranean Challenge seek novel approaches and new insights for discovering and demonstrating these innovative technologies, to help close critical gaps for robotic operations in complex urban and underground environments.

[ UPenn ] Continue reading

Posted in Human Robots

#438755 Soft Legged Robot Uses Pneumatic ...

Soft robots are inherently safe, highly resilient, and potentially very cheap, making them promising for a wide array of applications. But development on them has been a bit slow relative to other areas of robotics, at least partially because soft robots can’t directly benefit from the massive increase in computing power and sensor and actuator availability that we’ve seen over the last few decades. Instead, roboticists have had to get creative to find ways of achieving the functionality of conventional robotics components using soft materials and compatible power sources.

In the current issue of Science Robotics, researchers from UC San Diego demonstrate a soft walking robot with four legs that moves with a turtle-like gait controlled by a pneumatic circuit system made from tubes and valves. This air-powered nervous system can actuate multiple degrees of freedom in sequence from a single source of pressurized air, offering a huge reduction in complexity and bringing a very basic form of decision making onto the robot itself.

Generally, when people talk about soft robots, the robots are only mostly soft. There are some components that are very difficult to make soft, including pressure sources and the necessary electronics to direct that pressure between different soft actuators in a way that can be used for propulsion. What’s really cool about this robot is that researchers have managed to take a pressure source (either a single tether or an onboard CO2 cartridge) and direct it to four different legs, each with three different air chambers, using an oscillating three valve circuit made entirely of soft materials.

Photo: UCSD

The pneumatic circuit that powers and controls the soft quadruped.

The inspiration for this can be found in biology—natural organisms, including quadrupeds, use nervous system components called central pattern generators (CPGs) to prompt repetitive motions with limbs that are used for walking, flying, and swimming. This is obviously more complicated in some organisms than in others, and is typically mediated by sensory feedback, but the underlying structure of a CPG is basically just a repeating circuit that drives muscles in sequence to produce a stable, continuous gait. In this case, we’ve got pneumatic muscles being driven in opposing pairs, resulting in a diagonal couplet gait, where diagonally opposed limbs rotate forwards and backwards at the same time.

Diagram: Science Robotics

(J) Pneumatic logic circuit for rhythmic leg motion. A constant positive pressure source (P+) applied to three inverter components causes a high-pressure state to propagate around the circuit, with a delay at each inverter. While the input to one inverter is high, the attached actuator (i.e., A1, A2, or A3) is inflated. This sequence of high-pressure states causes each pair of legs of the robot to rotate in a direction determined by the pneumatic connections. (K) By reversing the sequence of activation of the pneumatic oscillator circuit, the attached actuators inflate in a new sequence (A1, A3, and A2), causing (L) the legs of the robot to rotate in reverse. (M) Schematic bottom view of the robot with the directions of leg motions indicated for forward walking.

Diagram: Science Robotics

Each of the valves acts as an inverter by switching the normally closed half (top) to open and the normally open half (bottom) to closed.

The circuit itself is made up of three bistable pneumatic valves connected by tubing that acts as a delay by providing resistance to the gas moving through it that can be adjusted by altering the tube’s length and inner diameter. Within the circuit, the movement of the pressurized gas acts as both a source of energy and as a signal, since wherever the pressure is in the circuit is where the legs are moving. The simplest circuit uses only three valves, and can keep the robot walking in one single direction, but more valves can add more complex leg control options. For example, the researchers were able to use seven valves to tune the phase offset of the gait, and even just one additional valve (albeit of a slightly more complex design) could enable reversal of the system, causing the robot to walk backwards in response to input from a soft sensor. And with another complex valve, a manual (tethered) controller could be used for omnidirectional movement.

This work has some similarities to the rover that JPL is developing to explore Venus—that rover isn’t a soft robot, of course, but it operates under similar constraints in that it can’t rely on conventional electronic systems for autonomous navigation or control. It turns out that there are plenty of clever ways to use mechanical (or in this case, pneumatic) intelligence to make robots with relatively complex autonomous behaviors, meaning that in the future, soft (or soft-ish) robots could find valuable roles in situations where using a non-compliant system is not a good option.

For more on why we should be so excited about soft robots and just how soft a soft robot needs to be, we spoke with Michael Tolley, who runs the Bioinspired Robotics and Design Lab at UCSD, and Dylan Drotman, the paper’s first author.

IEEE Spectrum: What can soft robots do for us that more rigid robotic designs can’t?

Michael Tolley: At the very highest level, one of the fundamental assumptions of robotics is that you have rigid bodies connected at joints, and all your motion happens at these joints. That's a really nice approach because it makes the math easy, frankly, and it simplifies control. But when you look around us in nature, even though animals do have bones and joints, the way we interact with the world is much more complicated than that simple story. I’m interested in where we can take advantage of material properties in robotics. If you look at robots that have to operate in very unknown environments, I think you can build in some of the intelligence for how to deal with those environments into the body of the robot itself. And that’s the category this work really falls under—it's about navigating the world.

Dylan Drotman: Walking through confined spaces is a good example. With the rigid legged robot, you would have to completely change the way that the legs move to walk through a confined space, while if you have flexible legs, like the robot in our paper, you can use relatively simple control strategies to squeeze through an area you wouldn’t be able to get through with a rigid system.

How smart can a soft robot get?

Drotman: Right now we have a sensor on the front that's connected through a fluidic transmission to a bistable valve that causes the robot to reverse. We could add other sensors around the robot to allow it to change direction whenever it runs into an obstacle to effectively make an electronics-free version of a Roomba.

Tolley: Stepping back a little bit from that, one could make an argument that we’re using basic memory elements to generate very basic signals. There’s nothing in principle that would stop someone from making a pneumatic computer—it’s just very complicated to make something that complex. I think you could build on this and do more intelligent decision making, but using this specific design and the components we’re using, it’s likely to be things that are more direct responses to the environment.

How well would robots like these scale down?

Drotman: At the moment we’re manufacturing these components by hand, so the idea would be to make something more like a printed circuit board instead, and looking at how the channel sizes and the valve design would affect the actuation properties. We’ll also be coming up with new circuits, and different designs for the circuits themselves.

Tolley: Down to centimeter or millimeter scale, I don’t think you’d have fundamental fluid flow problems. I think you’re going to be limited more by system design constraints. You’ll have to be able to locomote while carrying around your pressure source, and possibly some other components that are also still rigid. When you start to talk about really small scales, though, it's not as clear to me that you really need an intrinsically soft robot. If you think about insects, their structural geometry can make them behave like they’re soft, but they’re not intrinsically soft.

Should we be thinking about soft robots and compliant robots in the same way, or are they fundamentally different?

Tolley: There’s certainly a connection between the two. You could have a compliant robot that behaves in a very similar way to an intrinsically soft robot, or a robot made of intrinsically soft materials. At that point, it comes down to design and manufacturing and practical limitations on what you can make. I think when you get down to small scales, the two sort of get connected.

There was some interesting work several years ago on using explosions to power soft robots. Is that still a thing?

Tolley: One of the opportunities with soft robots is that with material compliance, you have the potential to store energy. I think there’s exciting potential there for rapid motion with a soft body. Combustion is one way of doing that with power coming from a chemical source all at once, but you could also use a relatively weak muscle that over time stores up energy in a soft body and then releases it.

Is it realistic to expect complete softness from soft robots, or will they likely always have rigid components because they have to store or generate and move pressurized gas somehow?

Tolley: If you look in nature, you do have soft pumps like the heart, but although it’s soft, it’s still relatively stiff. Like, if you grab a heart, it’s not totally squishy. I haven’t done it, but I’d imagine. If you have a container that you’re pressurizing, it has to be stiff enough to not just blow up like a balloon. Certainly pneumatics or hydraulics are not the only way to go for soft actuators; there has been some really nice work on smart muscles and smart materials like hydraulic electrostatic (HASEL) actuators. They seem promising, but all of these actuators have challenges. We’ve chosen to stick with pressurized pneumatics in the near term; longer term, I think you’ll start to see more of these smart material actuators become more practical.

Personally, I don’t have any problem with soft robots having some rigid components. Most animals on land have some rigid components, but they can still take advantage of being soft, so it’s probably going to be a combination. But I do also like the vision of making an entirely soft, squishy thing. Continue reading

Posted in Human Robots

#438506 How humans can build better teamwork ...

As human interaction with robots and artificial intelligence increases exponentially in areas like healthcare, manufacturing, transportation, space exploration, defense technologies, information about how humans and autonomous systems work within teams remains scarce. Continue reading

Posted in Human Robots

#437992 This Week’s Awesome Tech Stories From ...

ARTIFICIAL INTELLIGENCE
This Chinese Lab Is Aiming for Big AI Breakthroughs
Will Knight | Wired
“China produces as many artificial intelligence researchers as the US, but it lags in key fields like machine learning. The government hopes to make up ground. …It set AI researchers the goal of making ‘fundamental breakthroughs by 2025’ and called for the country to be ‘the world’s primary innovation center by 2030.’ BAAI opened a year later, in Zhongguancun, a neighborhood of Beijing designed to replicate US innovation hubs such as Boston and Silicon Valley.”

ENVIRONMENT
What Elon Musk’s $100 Million Carbon Capture Prize Could Mean
James Temple | MIT Technology Review
“[Elon Musk] announced on Twitter that he plans to give away $100 million of [his $180 billion net worth] as a prize for the ‘best carbon capture technology.’ …Another $100 million could certainly help whatever venture, or ventures, clinch Musk’s prize. But it’s a tiny fraction of his wealth and will also only go so far. …Money aside, however, one thing Musk has a particular knack for is generating attention. And this is a space in need of it.”

HEALTH
Synthetic Cornea Helped a Legally Blind Man Regain His Sight
Steve Dent | Engadget
“While the implant doesn’t contain any electronics, it could help more people than any robotic eye. ‘After years of hard work, seeing a colleague implant the CorNeat KPro with ease and witnessing a fellow human being regain his sight the following day was electrifying and emotionally moving, there were a lot of tears in the room,’ said CorNeat Vision co-founder Dr. Gilad Litvin.”

BIOTECH
MIT Develops Method for Lab-Grown Plants That May Eventually Lead to Alternatives to Forestry and Farming
Darrell Etherington | TechCrunch
“If the work of these researchers can eventually be used to create a way to produce lab-grown wood for use in construction and fabrication in a way that’s scalable and efficient, then there’s tremendous potential in terms of reducing the impact on forestry globally. Eventually, the team even theorizes you could coax the growth of plant-based materials into specific target shapes, so you could also do some of the manufacturing in the lab, by growing a wood table directly for instance.”

AUTOMATION
FAA Approves First Fully Automated Commercial Drone Flights
Andy Pasztor and Katy Stech Ferek | The Wall Street Journal
“US aviation regulators have approved the first fully automated commercial drone flights, granting a small Massachusetts-based company permission to operate drones without hands-on piloting or direct observation by human controllers or observers. …The company’s Scout drones operate under predetermined flight programs and use acoustic technology to detect and avoid drones, birds, and other obstacles.”

SPACE
China’s Surging Private Space Industry Is Out to Challenge the US
Neel V. Patel | MIT Technology Review
“[The Ceres-1] was a commercial rocket—only the second from a Chinese company ever to go into space. And the launch happened less than three years after the company was founded. The achievement is a milestone for China’s fledgling—but rapidly growing—private space industry, an increasingly critical part of the country’s quest to dethrone the US as the world’s preeminent space power.”

CRYPTOCURRENCY
Janet Yellen Will Consider Limiting Use of Cryptocurrency
Timothy B. Lee | Ars Technica
“Cryptocurrencies could come under renewed regulatory scrutiny over the next four years if Janet Yellen, Joe Biden’s pick to lead the Treasury Department, gets her way. During Yellen’s Tuesday confirmation hearing before the Senate Finance Committee, Sen. Maggie Hassan (D-N.H.) asked Yellen about the use of cryptocurrency by terrorists and other criminals. ‘Cryptocurrencies are a particular concern,’ Yellen responded. ‘I think many are used—at least in a transactions sense—mainly for illicit financing.’i”

SCIENCE
Secret Ingredient Found to Power Supernovas
Thomas Lewton | Quanta
“…Only in the last few years, with the growth of supercomputers, have theorists had enough computing power to model massive stars with the complexity needed to achieve explosions. …These new simulations are giving researchers a better understanding of exactly how supernovas have shaped the universe we see today.”

Image Credit: Ricardo Gomez Angel / Unsplash Continue reading

Posted in Human Robots

#437974 China Wants to Be the World’s AI ...

China’s star has been steadily rising for decades. Besides slashing extreme poverty rates from 88 percent to under 2 percent in just 30 years, the country has become a global powerhouse in manufacturing and technology. Its pace of growth may slow due to an aging population, but China is nonetheless one of the world’s biggest players in multiple cutting-edge tech fields.

One of these fields, and perhaps the most significant, is artificial intelligence. The Chinese government announced a plan in 2017 to become the world leader in AI by 2030, and has since poured billions of dollars into AI projects and research across academia, government, and private industry. The government’s venture capital fund is investing over $30 billion in AI; the northeastern city of Tianjin budgeted $16 billion for advancing AI; and a $2 billion AI research park is being built in Beijing.

On top of these huge investments, the government and private companies in China have access to an unprecedented quantity of data, on everything from citizens’ health to their smartphone use. WeChat, a multi-functional app where people can chat, date, send payments, hail rides, read news, and more, gives the CCP full access to user data upon request; as one BBC journalist put it, WeChat “was ahead of the game on the global stage and it has found its way into all corners of people’s existence. It could deliver to the Communist Party a life map of pretty much everybody in this country, citizens and foreigners alike.” And that’s just one (albeit big) source of data.

Many believe these factors are giving China a serious leg up in AI development, even providing enough of a boost that its progress will surpass that of the US.

But there’s more to AI than data, and there’s more to progress than investing billions of dollars. Analyzing China’s potential to become a world leader in AI—or in any technology that requires consistent innovation—from multiple angles provides a more nuanced picture of its strengths and limitations. In a June 2020 article in Foreign Affairs, Oxford fellows Carl Benedikt Frey and Michael Osborne argued that China’s big advantages may not actually be that advantageous in the long run—and its limitations may be very limiting.

Moving the AI Needle
To get an idea of who’s likely to take the lead in AI, it could help to first consider how the technology will advance beyond its current state.

To put it plainly, AI is somewhat stuck at the moment. Algorithms and neural networks continue to achieve new and impressive feats—like DeepMind’s AlphaFold accurately predicting protein structures or OpenAI’s GPT-3 writing convincing articles based on short prompts—but for the most part these systems’ capabilities are still defined as narrow intelligence: completing a specific task for which the system was painstakingly trained on loads of data.

(It’s worth noting here that some have speculated OpenAI’s GPT-3 may be an exception, the first example of machine intelligence that, while not “general,” has surpassed the definition of “narrow”; the algorithm was trained to write text, but ended up being able to translate between languages, write code, autocomplete images, do math, and perform other language-related tasks it wasn’t specifically trained for. However, all of GPT-3’s capabilities are limited to skills it learned in the language domain, whether spoken, written, or programming language).

Both AlphaFold’s and GPT-3’s success was due largely to the massive datasets they were trained on; no revolutionary new training methods or architectures were involved. If all it was going to take to advance AI was a continuation or scaling-up of this paradigm—more input data yields increased capability—China could well have an advantage.

But one of the biggest hurdles AI needs to clear to advance in leaps and bounds rather than baby steps is precisely this reliance on extensive, task-specific data. Other significant challenges include the technology’s fast approach to the limits of current computing power and its immense energy consumption.

Thus, while China’s trove of data may give it an advantage now, it may not be much of a long-term foothold on the climb to AI dominance. It’s useful for building products that incorporate or rely on today’s AI, but not for pushing the needle on how artificially intelligent systems learn. WeChat data on users’ spending habits, for example, would be valuable in building an AI that helps people save money or suggests items they might want to purchase. It will enable (and already has enabled) highly tailored products that will earn their creators and the companies that use them a lot of money.

But data quantity isn’t what’s going to advance AI. As Frey and Osborne put it, “Data efficiency is the holy grail of further progress in artificial intelligence.”

To that end, research teams in academia and private industry are working on ways to make AI less data-hungry. New training methods like one-shot learning and less-than-one-shot learning have begun to emerge, along with myriad efforts to make AI that learns more like the human brain.

While not insignificant, these advancements still fall into the “baby steps” category. No one knows how AI is going to progress beyond these small steps—and that uncertainty, in Frey and Osborne’s opinion, is a major speed bump on China’s fast-track to AI dominance.

How Innovation Happens
A lot of great inventions have happened by accident, and some of the world’s most successful companies started in garages, dorm rooms, or similarly low-budget, nondescript circumstances (including Google, Facebook, Amazon, and Apple, to name a few). Innovation, the authors point out, often happens “through serendipity and recombination, as inventors and entrepreneurs interact and exchange ideas.”

Frey and Osborne argue that although China has great reserves of talent and a history of building on technologies conceived elsewhere, it doesn’t yet have a glowing track record in terms of innovation. They note that of the 100 most-cited patents from 2003 to present, none came from China. Giants Tencent, Alibaba, and Baidu are all wildly successful in the Chinese market, but they’re rooted in technologies or business models that came out of the US and were tweaked for the Chinese population.

“The most innovative societies have always been those that allowed people to pursue controversial ideas,” Frey and Osborne write. China’s heavy censorship of the internet and surveillance of citizens don’t quite encourage the pursuit of controversial ideas. The country’s social credit system rewards people who follow the rules and punishes those who step out of line. Frey adds that top-down execution of problem-solving is effective when the problem at hand is clearly defined—and the next big leaps in AI are not.

It’s debatable how strongly a culture of social conformism can impact technological innovation, and of course there can be exceptions. But a relevant historical example is the Soviet Union, which, despite heavy investment in science and technology that briefly rivaled the US in fields like nuclear energy and space exploration, ended up lagging far behind primarily due to political and cultural factors.

Similarly, China’s focus on computer science in its education system could give it an edge—but, as Frey told me in an email, “The best students are not necessarily the best researchers. Being a good researcher also requires coming up with new ideas.”

Winner Take All?
Beyond the question of whether China will achieve AI dominance is the issue of how it will use the powerful technology. Several of the ways China has already implemented AI could be considered morally questionable, from facial recognition systems used aggressively against ethnic minorities to smart glasses for policemen that can pull up information about whoever the wearer looks at.

This isn’t to say the US would use AI for purely ethical purposes. The military’s Project Maven, for example, used artificially intelligent algorithms to identify insurgent targets in Iraq and Syria, and American law enforcement agencies are also using (mostly unregulated) facial recognition systems.

It’s conceivable that “dominance” in AI won’t go to one country; each nation could meet milestones in different ways, or meet different milestones. Researchers from both countries, at least in the academic sphere, could (and likely will) continue to collaborate and share their work, as they’ve done on many projects to date.

If one country does take the lead, it will certainly see some major advantages as a result. Brookings Institute fellow Indermit Gill goes so far as to say that whoever leads in AI in 2030 will “rule the world” until 2100. But Gill points out that in addition to considering each country’s strengths, we should consider how willing they are to improve upon their weaknesses.

While China leads in investment and the US in innovation, both nations are grappling with huge economic inequalities that could negatively impact technological uptake. “Attitudes toward the social change that accompanies new technologies matter as much as the technologies, pointing to the need for complementary policies that shape the economy and society,” Gill writes.

Will China’s leadership be willing to relax its grip to foster innovation? Will the US business environment be enough to compete with China’s data, investment, and education advantages? And can both countries find a way to distribute technology’s economic benefits more equitably?

Time will tell, but it seems we’ve got our work cut out for us—and China does too.

Image Credit: Adam Birkett on Unsplash Continue reading

Posted in Human Robots