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, Prof. Jan Peters (full professor for Intelligent Autonomous Systems at the Computer Science Department of the Technical University of Darmstadt) investigates with you 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.
Prof. Peters discusses 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. He discusses 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, robot juggling) to playing robot table tennis against a human being and manipulation of various objects.
Tuesday, 06-21-2022, 05:00 p.m. – 06:00 p.m., virtual event
Further details: https://www.i-aida.org/events/robot-learning/.
This is an event of I-AIDA – International Artificial Intelligence Doctoral Academy.