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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

Elderly Bodily Assistance Robot (E-BAR) – Revolutionizing Eldercare with Robotics Technology

Engineers built E-BAR, a mobile robot designed to physically support the elderly and prevent them from falling as they move around their homes. E-BAR acts as a set of robotic handlebars that follows a person from behind, allowing them to walk independently or lean on the robot’s arms for support.

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As the world’s population ages, the need for effective eldercare solutions becomes increasingly pressing. In response, a team of MIT engineers has developed the Elderly Bodily Assistance Robot (E-BAR), a mobile robot designed to physically support elderly individuals and prevent falls.

With its sleek design and advanced features, E-BAR represents a significant breakthrough in robotics technology. The robot consists of a heavy base with omnidirectional wheels that allows it to move freely in any direction. Extending from the base is an articulated body made up of 18 interconnected bars that can reconfigure like a foldable crane to lift individuals from sitting to standing positions and vice versa.

One of the key features of E-BAR is its ability to catch users if they fall, without the need for wearable devices or harnesses. This is achieved through two arms with handlebars that stretch out in a U-shape, allowing individuals to stand between and lean against them for support. Each arm is embedded with airbags made from a soft yet grippable material that can inflate instantly to catch users on impact.

The E-BAR team conducted laboratory tests with an older adult volunteer, simulating various household scenarios such as picking up objects from the ground and reaching items off shelves. The results were promising, demonstrating the robot’s ability to actively support individuals while maintaining balance.

While the current version of E-BAR does not incorporate fall-prediction capabilities, another project in Asada’s lab is working on developing algorithms with machine learning to control a new robot in response to users’ real-time fall risk levels. This integration would enable E-BAR to provide even more comprehensive support and prevention services.

The development of E-BAR represents an exciting step towards revolutionizing eldercare with robotics technology. As the population continues to age, innovative solutions like E-BAR will become increasingly essential for ensuring the health, safety, and well-being of our elderly loved ones.

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Artificial Intelligence

Revolutionizing Rehabilitation with Extended Reality Boccia: A Game-Changer for Older Adults

A team has developed Boccia XR, a rehabilitation program using extended reality technology that can be introduced even in environments with limited space.

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The world of sports has long been fascinated by the unique charm of boccia – a Paralympic game that transcends age and ability barriers. A team from Osaka Metropolitan University has taken this phenomenon a step further with Extended Reality (XR) Boccia, an innovative rehabilitation program that combines physical exercise with emotional benefits for older adults. Developed by Associate Professor Masataka Kataoka’s research group, XR Boccia offers a fresh alternative to traditional boccia and treadmill walking, making it perfect for environments with limited space.

The researchers conducted an intriguing study to investigate the effects of XR Boccia on participants over 65. The findings reveal that both XR Boccia and traditional boccia showed significant improvements in mood, vitality, and energy among participants after experiencing these programs. Notably, there was no substantial difference in lower limb muscle activity during any of the exercises, although a notable increase in rectus femoris muscle activity (which helps extend the knee) was observed in both types of boccia compared to treadmill walking.

The implications of this research are groundbreaking. Associate Professor Kataoka noted that XR Boccia could be an effective rehabilitation exercise for older adults, boasting both physical and emotional benefits. Given its adaptability and practicality, it’s suitable for indoor environments like hospitals and nursing care facilities. The researchers aim to further investigate long-term results in a larger population of older adults and continue updating the XR program.

The study was published in PLOS One, shedding light on this innovative approach to rehabilitation. With XR Boccia, we may be witnessing a new chapter in the journey towards better health and happiness for older adults, one game at a time.

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Artificial Intelligence

Riding the Tides: Scientists Develop Simple Algorithm for Underwater Robots to Harness Ocean Currents

Engineers have taught a simple submarine robot to take advantage of turbulent forces to propel itself through water.

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Researchers at Caltech have made a breakthrough in developing a simple algorithm for underwater robots to harness the power of ocean currents. Led by John Dabiri, the Centennial Professor of Aeronautics and Mechanical Engineering, the team has successfully created a system that allows small autonomous underwater vehicles (AUVs) to ride on turbulent water currents rather than fighting against them.

The researchers began by studying how jellyfish navigate through the ocean using their unique ability to traverse and plumb the depths. They outfitted these creatures with electronics and prosthetic “hats” to carry small payloads and report findings back to the surface. However, they soon realized that jellyfish do not have a brain and therefore cannot make decisions about how to navigate.

To address this limitation, Dabiri’s team developed what would be considered the equivalent of a brain for an AUV using artificial intelligence (AI). This allowed the robots to make decisions underwater and potentially take advantage of environmental flows. However, they soon discovered that AI was not the most efficient solution for their problem.

Enter Peter Gunnarson, a former graduate student who returned to Dabiri’s lab with a simpler approach. He attached an accelerometer to CARL-Bot, an AUV developed years ago as part of his work on incorporating artificial intelligence into its navigation technique. By measuring how CARL-Bot was being pushed around by vortex rings (underwater equivalents of smoke rings), Gunnarson noticed that the robot would occasionally get caught up in a vortex ring and be propelled clear across the tank.

The team then developed simple commands to help CARL-Bot detect the relative location of a vortex ring and position itself to catch a ride. Alternatively, the bot can decide to get out of the way if it does not want to be pushed by a particular vortex ring. This process involves elements of biomimicry, mimicking nature’s ability to use environmental flows for energy conservation.

Dabiri hopes to marry this work with his hybrid jellyfish project, which aims to demonstrate a similar capability to take advantage of environmental flows and move more efficiently through the water. With this breakthrough, underwater robots can now ride the tides, reducing energy expenditure and increasing their efficiency in navigating the ocean depths.

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