Toyota Research Institute (TRI) recently revealed a generative AI approach, based on Diffusion Policy, that facilitates the quick teaching of dexterous skills to robots. This method is an advancement towards creating “Large Behavior Models (LBMs)” for robots, drawing parallels to the Large Language Models (LLMs) that have impacted conversational AI.
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An innovative bimanual robot displays tactile sensitivity close to human-level dexterity using AI to inform its actions.
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New work from Carnegie Mellon University has enabled robots to learn household chores by watching videos of people performing everyday tasks in their homes. The research could help improve the utility of robots in the home, allowing them to assist people with tasks like cooking and cleaning. Two robots successfully learned 12 tasks including opening a drawer, oven door and lid; taking a pot off the stove; and picking up a telephone, vegetable or can of soup.
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Engineering researchers at the University of Waterloo are successfully using a robot to help keep children with learning disabilities focused on their work. This was one of the key results in a new study that also found both the youngsters and their instructors valued the positive classroom contributions made by the robot.
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Exploring a new way to teach robots, Princeton researchers have found that human-language descriptions of tools can accelerate the learning of a simulated robotic arm lifting and using a variety of tools.
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Intel Labs, in collaboration with the Italian Institute of Technology and the Technical University of Munich, has introduced a new approach to neural network-based object learning. It specifically targets future applications like robotic assistants that interact with unconstrained environments, including in logistics, healthcare or elderly care.
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An international collaboration led by Philip Walther, a team of experimental physicists from the University of Vienna, together with theoreticians from the University of Innsbruck, the Austrian Academy of Sciences, Leiden University, and the German Aerospace Center, have been successful in experimentally proving for the first time a speed-up in the actual robot’s learning time.