Help Privacy Policy Disclaimer
  Advanced SearchBrowse





Exploration in Learning of Motor Skills for Robotics


Peters,  J
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available

Peters, J. (2011). Exploration in Learning of Motor Skills for Robotics. Talk presented at Exploration and Curiosity in Robot Learning and Inference (Dagstuhl Seminar 11131). Dagstuhl, Germany. 2011-03-27 - 2011-04-01. doi:10.4230/DagRep.1.3.67.

Cite as: https://hdl.handle.net/21.11116/0000-0004-7E9D-F
Intelligent 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 elementary 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. A large number of real-robot examples will be demonstrated ranging from Learning of Ball-Paddling, Ball-In-A-Cup, Darts, Table Tennis to Grasping.