Tag Archives: law
#437824 Video Friday: These Giant Robots Are ...
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!):
ACRA 2020 – December 8-10, 2020 – [Online]
Let us know if you have suggestions for next week, and enjoy today's videos.
“Who doesn’t love giant robots?”
Luma, is a towering 8 metre snail which transforms spaces with its otherworldly presence. Another piece, Triffid, stands at 6 metres and its flexible end sweeps high over audiences’ heads like an enchanted plant. The movement of the creatures is inspired by the flexible, wiggling and contorting motions of the animal kingdom and is designed to provoke instinctive reactions and emotions from the people that meet them. Air Giants is a new creative robotic studio founded in 2020. They are based in Bristol, UK, and comprise a small team of artists, roboticists and software engineers. The studio is passionate about creating emotionally effective motion at a scale which is thought-provoking and transporting, as well as expanding the notion of what large robots can be used for.
Here’s a behind the scenes and more on how the creatures work.
[ Air Giants ]
Thanks Emma!
If the idea of submerging a very expensive sensor payload being submerged in a lake makes you as uncomfortable as it makes me, this is not the video for you.
[ ANYbotics ]
As the pandemic continues on, the measures due to this health crisis are increasingly stringent, and working from home continues to be promoted and solicited by many companies, Pepper will allow you to keep in touch with your relatives or even your colleagues.
[ Softbank ]
Fairly impressive footwork from Tencent Robotics.
Although, LittleDog was doing that like a decade ago:
[ Tencent ]
It's been long enough since I've been able to go out for boba tea that a robotic boba tea kiosk seems like a reasonable thing to get for my living room.
[ Bobacino ] via [ Gizmodo ]
Road construction and maintenance is challenging and dangerous work. Pioneer Industrial Systems has spent over twenty years designing custom robotic systems for industrial manufacturers around the world. These robotic systems greatly improve safety and increase efficiency. Now they’re taking that expertise on the road, with the Robotic Maintenance Vehicle. This base unit can be mounted on a truck or trailer, and utilizes various modules to perform a variety of road maintenance tasks.
[ Pioneer ]
Extend Robotics arm uses cloud-based teleoperation software, featuring human-like dexterity and intelligence, with multiple applications in healthcare, utilities and energy
[ Extend Robotics ]
ARC, short for “AI, Robot, Cloud,” includes the latest algorithms and high precision data required for human-robot coexistence. Now with ultra-low latency networks, many robots can simultaneously become smarter, just by connecting to ARC. “ARC Eye” serves as the eyes for all robots, accurately determining the current location and route even indoors where there is no GPS access. “ARC Brain” is the computing system shared simultaneously by all robots, which plans and processes movement, localization, and task performance for the robot.
[ Naver Labs ]
How can we re-imagine urban infrastructures with cutting-edge technologies? Listen to this webinar from Ger Baron, Amsterdam’s CTO, and Senseable City Lab’s researchers, on how MIT and Amsterdam Institute for Advanced Metropolitan Solutions (AMS Institute) are reimagining Amsterdam’s canals with the first fleet of autonomous boats.
[ MIT ]
Join Guy Burroughes in this webinar recording to hear about Spot, the robot dog created by Boston Dynamics, and how RACE plan to use it in nuclear decommissioning and beyond.
[ UKAEA ]
This GRASP on Robotics seminar comes from Marco Pavone at Stanford University, “On Safe and Efficient Human-robot interactions via Multimodal Intent Modeling and Reachability-based Safety Assurance.”
In this talk I will present a decision-making and control stack for human-robot interactions by using autonomous driving as a motivating example. Specifically, I will first discuss a data-driven approach for learning multimodal interaction dynamics between robot-driven and human-driven vehicles based on recent advances in deep generative modeling. Then, I will discuss how to incorporate such a learned interaction model into a real-time, interaction-aware decision-making framework. The framework is designed to be minimally interventional; in particular, by leveraging backward reachability analysis, it ensures safety even when other cars defy the robot's expectations without unduly sacrificing performance. I will present recent results from experiments on a full-scale steer-by-wire platform, validating the framework and providing practical insights. I will conclude the talk by providing an overview of related efforts from my group on infusing safety assurances in robot autonomy stacks equipped with learning-based components, with an emphasis on adding structure within robot learning via control-theoretical and formal methods.
[ UPenn ]
Autonomous Systems Failures: Who is Legally and Morally Responsible? Sponsored by Northwestern University’s Law and Technology Initiative and AI@NU, the event was moderated by Dan Linna and included Northwestern Engineering's Todd Murphey, University of Washington Law Professor Ryan Calo, and Google Senior Research Scientist Madeleine Clare Elish.
[ Northwestern ] Continue reading
Posted in Human Robots
#437791 Is the Pandemic Spurring a Robot ...
“Are robots really destined to take over restaurant kitchens?” This was the headline of an article published by Eater four years ago. One of the experts interviewed was Siddhartha Srinivasa, at the time professor of the Robotics Institute at Carnegie Mellon University and currently director of Robotics and AI for Amazon. He said, “I’d love to make robots unsexy. It’s weird to say this, but when something becomes unsexy, it means that it works so well that you don’t have to think about it. You don’t stare at your dishwasher as it washes your dishes in fascination, because you know it’s gonna work every time… I want to get robots to that stage of reliability.”
Have we managed to get there over the last four years? Are robots unsexy yet? And how has the pandemic changed the trajectory of automation across industries?
The Covid Effect
The pandemic has had a massive economic impact all over the world, and one of the problems faced by many companies has been keeping their businesses running without putting employees at risk of infection. Many organizations are seeking to remain operational in the short term by automating tasks that would otherwise be carried out by humans. According to Digital Trends, since the start of the pandemic we have seen a significant increase in automation efforts in manufacturing, meat packing, grocery stores and more. In a June survey, 44 percent of corporate financial officers said they were considering more automation in response to coronavirus.
MIT economist David Autor described the economic crisis and the Covid-19 pandemic as “an event that forces automation.” But he added that Covid-19 created a kind of disruption that has forced automation in sectors and activities with a shortage of workers, while at the same time there has been no reduction in demand. This hasn’t taken place in hospitality, where demand has practically disappeared, but it is still present in agriculture and distribution. The latter is being altered by the rapid growth of e-commerce, with more efficient and automated warehouses that can provide better service.
China Leads the Way
China is currently in a unique position to lead the world’s automation economy. Although the country boasts a huge workforce, labor costs have multiplied by 10 over the past 20 years. As the world’s factory, China has a strong incentive to automate its manufacturing sector, which enjoys a solid leadership in high quality products. China is currently the largest and fastest-growing market in the world for industrial robotics, with a 21 percent increase up to $5.4 billion in 2019. This represents one third of global sales. As a result, Chinese companies are developing a significant advantage in terms of learning to work with metallic colleagues.
The reasons behind this Asian dominance are evident: the population has a greater capacity and need for tech adoption. A large percentage of the population will soon be of retirement age, without an equivalent younger demographic to replace it, leading to a pressing need to adopt automation in the short term.
China is well ahead of other countries in restaurant automation. As reported in Bloomberg, in early 2020 UBS Group AG conducted a survey of over 13,000 consumers in different countries and found that 64 percent of Chinese participants had ordered meals through their phones at least once a week, compared to a mere 17 percent in the US. As digital ordering gains ground, robot waiters and chefs are likely not far behind. The West harbors a mistrust towards non-humans that the East does not.
The Robot Evolution
The pandemic was a perfect excuse for robots to replace us. But despite the hype around this idea, robots have mostly disappointed during the pandemic.
Just over 66 different kinds of “social” robots have been piloted in hospitals, health centers, airports, office buildings, and other public and private spaces in response to the pandemic, according to a study from researchers at Pompeu Fabra University (Barcelona, Spain). Their survey looked at 195 robot deployments across 35 countries including China, the US, Thailand, and Hong Kong.
But if the “robot revolution” is a movement in which automation, robotics, and artificial intelligence proliferate through the value chain of various industries, bringing a paradigm shift in how we produce, consume, and distribute products—it hasn’t happened yet.
But there’s a more nuanced answer: rather than a revolution, we’re seeing an incremental robot evolution. It’s a trend that will likely accelerate over the next five years, particularly when 5G takes center stage and robotics as a field leaves behind imitation and evolves independently.
Automation Anxiety
Why don’t we finally welcome the long-promised robotic takeover? Despite progress in AI and increased adoption of industrial robots, consumer-facing robotic products are not nearly as ubiquitous as popular culture predicted decades ago. As Amara’s Law says: “We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run.” It seems we are living through the Gartner hype cycle.
People have a complicated relationship with robots, torn between admiring them, fearing them, rejecting them, and even boycotting them, as has happened in the automobile industry.
Retail robot in a Walmart store. Credit: Bossa Nova Robotics
Walmart terminated its contract with Bossa Nova and withdrew its 1,000 inventory robots from its stores because the company was concerned about how shoppers were reacting to seeing the six-foot robots in the aisles.
With road blocks like this, will the World Economic Forum’s prediction of almost half of tasks being carried out by machines by 2025 come to pass?
At the rate we’re going, it seems unlikely, even with the boost in automation caused by the pandemic. Robotics will continue to advance its capabilities, and will take over more human jobs as it does so, but it’s unlikely we’ll hit a dramatic inflection point that could be described as a “revolution.” Instead, the robot evolution will happen the way most societal change does: incrementally, with time for people to adapt both practically and psychologically.
For now though, robots are still pretty sexy.
Image Credit: charles taylor / Shutterstock.com Continue reading
Posted in Human Robots
#437751 Startup and Academics Find Path to ...
Engineers have been chasing a form of AI that could drastically lower the energy required to do typical AI things like recognize words and images. This analog form of machine learning does one of the key mathematical operations of neural networks using the physics of a circuit instead of digital logic. But one of the main things limiting this approach is that deep learning’s training algorithm, back propagation, has to be done by GPUs or other separate digital systems.
Now University of Montreal AI expert Yoshua Bengio, his student Benjamin Scellier, and colleagues at startup Rain Neuromorphics have come up with way for analog AIs to train themselves. That method, called equilibrium propagation, could lead to continuously learning, low-power analog systems of a far greater computational ability than most in the industry now consider possible, according to Rain CTO Jack Kendall.
Analog circuits could save power in neural networks in part because they can efficiently perform a key calculation, called multiply and accumulate. That calculation multiplies values from inputs according to various weights, and then it sums all those values up. Two fundamental laws of electrical engineering can basically do that, too. Ohm’s Law multiplies voltage and conductance to give current, and Kirchoff’s Current Law sums the currents entering a point. By storing a neural network’s weights in resistive memory devices, such as memristors, multiply-and-accumulate can happen completely in analog, potentially reducing power consumption by orders of magnitude.
The reason analog AI systems can’t train themselves today has a lot to do with the variability of their components. Just like real neurons, those in analog neural networks don’t all behave exactly alike. To do back propagation with analog components, you must build two separate circuit pathways. One going forward to come up with an answer (called inferencing), the other going backward to do the learning so that the answer becomes more accurate. But because of the variability of analog components, the pathways don't match up.
“You end up accumulating error as you go backwards through the network,” says Bengio. To compensate, a network would need lots of power-hungry analog-to-digital and digital-to-analog circuits, defeating the point of going analog.
Equilibrium propagation allows learning and inferencing to happen on the same network, partly by adjusting the behavior of the network as a whole. “What [equilibrium propagation] allows us to do is to say how we should modify each of these devices so that the overall circuit performs the right thing,” he says. “We turn the physical computation that is happening in the analog devices directly to our advantage.”
Right now, equilibrium propagation is only working in simulation. But Rain plans to have a hardware proof-of-principle in late 2021, according to CEO and cofounder Gordon Wilson. “We are really trying to fundamentally reimagine the hardware computational substrate for artificial intelligence, find the right clues from the brain, and use those to inform the design of this,” he says. The result could be what they call end-to-end analog AI systems that capable of running sophisticated robots or even playing a role in data centers. Both of those are currently considered beyond the capabilities of analog AI, which is now focused only on adding inferencing abilities to sensors and other low-power “edge” devices, while leaving the learning to GPUs. Continue reading
Posted in Human Robots
#437471 How Giving Robots a Hybrid, Human-Like ...
Squeezing a lot of computing power into robots without using up too much space or energy is a constant battle for their designers. But a new approach that mimics the structure of the human brain could provide a workaround.
The capabilities of most of today’s mobile robots are fairly rudimentary, but giving them the smarts to do their jobs is still a serious challenge. Controlling a body in a dynamic environment takes a surprising amount of processing power, which requires both real estate for chips and considerable amounts of energy to power them.
As robots get more complex and capable, those demands are only going to increase. Today’s most powerful AI systems run in massive data centers across far more chips than can realistically fit inside a machine on the move. And the slow death of Moore’s Law suggests we can’t rely on conventional processors getting significantly more efficient or compact anytime soon.
That prompted a team from the University of Southern California to resurrect an idea from more than 40 years ago: mimicking the human brain’s division of labor between two complimentary structures. While the cerebrum is responsible for higher cognitive functions like vision, hearing, and thinking, the cerebellum integrates sensory data and governs movement, balance, and posture.
When the idea was first proposed the technology didn’t exist to make it a reality, but in a paper recently published in Science Robotics, the researchers describe a hybrid system that combines analog circuits that control motion and digital circuits that govern perception and decision-making in an inverted pendulum robot.
“Through this cooperation of the cerebrum and the cerebellum, the robot can conduct multiple tasks simultaneously with a much shorter latency and lower power consumption,” write the researchers.
The type of robot the researchers were experimenting with looks essentially like a pole balancing on a pair of wheels. They have a broad range of applications, from hoverboards to warehouse logistics—Boston Dynamics’ recently-unveiled Handle robot operates on the same principles. Keeping them stable is notoriously tough, but the new approach managed to significantly improve all digital control approaches by radically improving the speed and efficiency of computations.
Key to bringing the idea alive was the recent emergence of memristors—electrical components whose resistance relies on previous input, which allows them to combine computing and memory in one place in a way similar to how biological neurons operate.
The researchers used memristors to build an analog circuit that runs an algorithm responsible for integrating data from the robot’s accelerometer and gyroscope, which is crucial for detecting the angle and velocity of its body, and another that controls its motion. One key advantage of this setup is that the signals from the sensors are analog, so it does away with the need for extra circuitry to convert them into digital signals, saving both space and power.
More importantly, though, the analog system is an order of magnitude faster and more energy-efficient than a standard all-digital system, the authors report. This not only lets them slash the power requirements, but also lets them cut the processing loop from 3,000 microseconds to just 6. That significantly improves the robot’s stability, with it taking just one second to settle into a steady state compared to more than three seconds using the digital-only platform.
At the minute this is just a proof of concept. The robot the researchers have built is small and rudimentary, and the algorithms being run on the analog circuit are fairly basic. But the principle is a promising one, and there is currently a huge amount of R&D going into neuromorphic and memristor-based analog computing hardware.
As often turns out to be the case, it seems like we can’t go too far wrong by mimicking the best model of computation we have found so far: our own brains.
Image Credit: Photos Hobby / Unsplash Continue reading
Posted in Human Robots