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Artificial Intelligence

Robot see, robot do: A Revolutionary System that Learns from How-to Videos

Researchers have developed a new robotic framework powered by artificial intelligence — called RHyME (Retrieval for Hybrid Imitation under Mismatched Execution) — that allows robots to learn tasks by watching a single how-to video.

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Robotics has long been plagued by the need for precise, step-by-step directions, making them finicky learners. However, researchers at Cornell University have developed a revolutionary new framework called RHyME (Retrieval for Hybrid Imitation under Mismatched Execution) that allows robots to learn tasks by watching a single how-to video. This groundbreaking system supercharges a robotic system to use its own memory and connect the dots when performing tasks it has viewed only once, drawing inspiration from videos it has seen.

The RHyME system is powered by artificial intelligence and can significantly reduce the time, energy, and money needed to train robots. According to researchers, one of the annoying things about working with robots is collecting so much data on the robot doing different tasks. This new approach, a branch of machine learning called “imitation learning,” allows humans to look at other people as inspiration, just like we do in real life.

Home robot assistants are still a long way off because they lack the wits to navigate the physical world and its countless contingencies. To get robots up to speed, researchers like Kushal Kedia and Sanjiban Choudhury are training them with what amounts to how-to videos – human demonstrations of various tasks in a lab setting. The hope with this approach is that robots will learn a sequence of tasks faster and be able to adapt to real-world environments.

“Our work is like translating French to English — we’re translating any given task from human to robot,” said senior author Sanjiban Choudhury, assistant professor of computer science. This translation task still faces a broader challenge: Humans move too fluidly for a robot to track and mimic, and training robots with video requires gobs of it.

RHyME is the team’s answer – a scalable approach that makes robots less finicky and more adaptive. It supercharges a robotic system to use its own memory and connect the dots when performing tasks it has viewed only once by drawing on videos it has seen. For example, a RHyME-equipped robot shown a video of a human fetching a mug from the counter and placing it in a nearby sink will comb its bank of videos and draw inspiration from similar actions – like grasping a cup and lowering a utensil.

RHyME paves the way for robots to learn multiple-step sequences while significantly lowering the amount of robot data needed for training. RHyME requires just 30 minutes of robot data; in a lab setting, robots trained using the system achieved a more than 50% increase in task success compared to previous methods, the researchers said.

With the development of RHyME, we may soon see robots that can learn and adapt to real-world environments with greater ease. This breakthrough has the potential to revolutionize various industries and aspects of our lives, making robots more efficient and effective.

Artificial Intelligence

Harnessing the Power of AI: Why Leashes are Better than Guardrails for Regulation

Many policy discussions on AI safety regulation have focused on the need to establish regulatory ‘guardrails’ to protect the public from the risks of AI technology. Experts now argue that, instead of imposing guardrails, policymakers should demand ‘leashes.’

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Harnessing the Power of AI: Why Leashes are Better than Guardrails for Regulation

For years, policymakers have debated the best way to regulate Artificial Intelligence (AI) to prevent its potential risks. A new paper by experts Cary Coglianese and Colton R. Crum proposes a game-changing approach: rather than imposing strict “guardrails” to control AI development, they suggest using flexible “leashes.” This management-based regulation would allow firms to innovate while ensuring public safety.

The authors argue that guardrails are not effective for AI due to its rapidly evolving nature and diverse applications. Social media, chatbots, autonomous vehicles, precision medicine, and fintech investment advisors are just a few examples of how AI is transforming industries. While offering numerous benefits, such as improved cancer detection, AI also poses risks like AV collisions, social media-induced suicides, and bias in digital formats.

Coglianese and Crum provide three case studies illustrating the potential harm from unregulated AI:

1. Autonomous vehicle (AV) crashes
2. Social media-related suicides
3. Bias and discrimination through AI-generated content

In each scenario, firms using AI tools would be expected to put their technology on a leash by implementing internal systems to mitigate potential harms. This flexible approach allows for technological innovation while ensuring that companies are accountable for the consequences of their actions.

Management-based regulation offers several advantages over guardrails:

* It can flexibly respond to AI’s novel uses and problems
* It enables technological exploration, discovery, and change
* It provides a tethered structure that helps prevent AI from “running away”

By embracing this leash-like approach, policymakers can harness the power of AI while minimizing its risks.

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Artificial Intelligence

A Breakthrough in Soft Robotics: Engineers Develop Self-Healing Muscle for Robots

Students recently unveiled their invention of a robotic actuator — the ‘muscle’ that converts energy into a robot’s physical movement — that has the ability to detect punctures or pressure, heal the injury and repair its damage-detecting ‘skin.’

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The University of Nebraska-Lincoln engineering team has made significant strides in developing soft robotics and wearable systems inspired by human and plant skin’s ability to self-heal injuries. Led by engineer Eric Markvicka, the team presented a groundbreaking paper at the IEEE International Conference on Robotics and Automation that showcased their innovative approach to creating an intelligent, self-healing artificial muscle.

The team’s strategy overcomes the long-standing problem of replicating traditional rigid systems using soft materials and incorporating nature-inspired design principles. Their multi-layer architecture enables the system to identify damage, pinpoint its location, and autonomously initiate a self-repair mechanism – all without external intervention.

The “muscle” or actuator features three layers: a damage detection layer composed of liquid metal microdroplets embedded in silicone elastomer, a self-healing component that uses thermoplastic elastomer to seal the wound, and an actuation layer that kick-starts the muscle’s motion when pressurized with water.

To begin the process, the team induces monitoring currents across the damage detection layer, which triggers formation of an electrical network between traces. Puncture or pressure damage causes this network to form, allowing the system to recognize and respond to the damage.

The next step is using electromigration – a phenomenon traditionally viewed as a hindrance in metallic circuits – to erase the newly formed electrical footprint. By further ramping up the current, the team can induce electromigration and thermal failure mechanisms that reset the damage detection network, effectively completing one cycle of damage and repair.

This breakthrough has far-reaching implications for various industries, particularly in agricultural states where robotics systems frequently encounter sharp objects. It could also revolutionize wearable health monitoring devices that must withstand daily wear and tear.

The technology has the potential to transform society more broadly by reducing electronic waste and mitigating environmental harm caused by consumer-based electronics’ short lifespans. Most consumer electronics have a lifespan of only one or two years, contributing billions of pounds of toxic waste each year.

“If we can begin to create materials that are able to passably and autonomously detect when damage has happened, and then initiate these self-repair mechanisms, it would really be transformative,” Markvicka said.

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Aerospace

“Mid-air Marvel: Caltech Engineers Create Transforming Robot That Flies and Rolls with Ease”

Engineers have developed a real-life Transformer that has the ‘brains’ to morph in midair, allowing the drone-like robot to smoothly roll away and begin its ground operations without pause. The increased agility and robustness of such robots could be particularly useful for commercial delivery systems and robotic explorers.

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Rewritten Article:

In a breakthrough that’s straight out of science fiction, a team of engineers at Caltech has developed a real-life Transformer that can morph in mid-air, allowing it to smoothly transition from flying to rolling on the ground. This innovative technology has far-reaching implications for commercial delivery systems and robotic explorers, making it an exciting development in the field of robotics.

The new robot, dubbed ATMO (aerially transforming morphobot), uses four thrusters to fly but can transform into a ground-rolling configuration using a single motor that lifts its thrusters up or down. This unique design allows ATMO to change its shape and function seamlessly, enabling it to adapt to various environments and situations.

According to Ioannis Mandralis, the lead author of the research paper published in Communications Engineering, “We designed and built a new robotic system inspired by nature – by the way that animals can use their bodies in different ways to achieve different types of locomotion.” For example, birds fly and then change their body morphology to slow themselves down and avoid obstacles. Mandralis adds, “Having the ability to transform in the air unlocks a lot of possibilities for improved autonomy and robustness.”

However, mid-air transformation also poses challenges due to complex aerodynamic forces that come into play both because the robot is close to the ground and because it is changing its shape as it morphs. Mory Gharib, the Hans W. Liepmann Professor of Aeronautics and Medical Engineering, notes that “Even though it seems simple when you watch a bird land and then run, in reality this is a problem that the aerospace industry has been struggling to deal with for probably more than 50 years.”

To better understand these complex aerodynamic forces, the researchers ran tests in Caltech’s drone lab using load cell experiments and smoke visualization. They fed those insights into the algorithm behind a new control system they created for ATMO, which uses advanced model predictive control to continuously predict how the system will behave in the near future and adjust its actions accordingly.

“The control algorithm is the biggest innovation in this paper,” Mandralis says. “Quadrotors use particular controllers because of how their thrusters are placed and how they fly. Here we introduce a dynamic system that hasn’t been studied before. As soon as the robot starts morphing, you get different dynamic couplings – different forces interacting with one another. And the control system has to be able to respond quickly to all of that.”

The potential applications of ATMO are vast and exciting, from commercial delivery systems to robotic explorers. With its unique ability to transform in mid-air and adapt to various environments, this technology has the potential to revolutionize the field of robotics and beyond.

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