Machine Learning of Robot Skills Jan Peters Computer Science Department – Technische Universitaet Darmstadt, Germany Robot Learning Group – Max-Planck Institute for Intelligent Systems, Germany Abstract: Autonomous robots that can assist humans in situations of daily life have been a long standing vision of robotics, artificial intelligence, and cognitive sciences. A first step towards this goal is to create robots that can learn tasks triggered by environmental context or higher level instruction. However, learning techniques have yet to live up to this promise as only few methods manage to scale to high-dimensional manipulator or humanoid robots. In this talk, we investigate a general framework suitable for learning motor skills in robotics which is based on the principles behind many analytical robotics approaches. It involves generating a representation of motor skills by parameterized motor primitive policies acting as building blocks of movement generation, and a learned task execution module that transforms these movements into motor commands. We discuss learning on three different levels of abstraction, i.e., learning for accurate control is needed to execute, learning of motor primitives is needed to acquire simple movements, and learning of the task-dependent „hyperparameters“ of these motor primitives allows learning complex tasks. We discuss task-appropriate learning approaches for imitation learning, model learning and reinforcement learning for robots with many degrees of freedom. Empirical evaluations on a several robot systems illustrate the effectiveness and applicability to learning control on an anthropomorphic robot arm. These robot motor skills range from toy examples (e.g., paddling a ball, ball-in-a-cup) to playing robot table tennis against a human being and manipulation of various objects. Bio: Jan Peters is a full professor (W3) for Intelligent Autonomous Systems at the Computer Science Department of the Technische Universitaet Darmstadt and at the same time a senior research scientist and group leader at the Max-Planck Institute for Intelligent Systems, where he heads the interdepartmental Robot Learning Group. Jan Peters has received the Dick Volz Best 2007 US PhD Thesis Runner-Up Award, the Robotics: Science & Systems – Early Career Spotlight, the INNS Young Investigator Award, and the IEEE Robotics & Automation Society’s Early Career Award as well as numerous best paper awards. In 2015, he received an ERC Starting Grant and in 2019, he was appointed as an IEEE Fellow.
Author: giuseppe
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Call for Virtual Rooms and Socials
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COVID-19 Update
In light of the current pandemic, the organizers are currently considering how best to run LOD2020. Even if it doesn’t go ahead physically, we expect to run a virtual version, so please continue to prepare papers for submission. More details will be posted on the website when they have been decided.
Paper Submission Deadline: June 4
All papers must be submitted using EasyChair. https://easychair.org/conferences/?conf=lod2020 Paper Submission Deadline: June 4, 2020 – Anywhere on Earth
Abstract Submission Deadline: June 4, 2020
All abstracts must be submitted using EasyChair Abstract Submission deadline: June 4, 2020 – Anywhere on Earth