From Motor Learning to Interaction Learning in Robots

Aus den Rezensionen: "... Dieses Buch ist eine Sammlung an wissenschaftlichen Beitragen, die Methoden diskutieren, wie dem Roboter Aufgaben beigebracht wer den konnen und wie solche Roboter optimalerweise gestaltet werden sollten. 5 Beitrage erlautern biologisch inspirierte Programmer Modelle, 7 Aufsatze diskutieren Roboter Lcrnstrategien und 8 befassen sich mit der Imitationsmethode, bei der man dem Roboter vormacht, was man von ihm erwartet. Ein aktuelles Buch fur Roboter Entwickler." (in: Bulletin SEV/VSE electrosuisse (Sonderausgabe), 22/October/2010)

• From Motor Learning to Interaction Learning in Robots.- From Motor Learning to Interaction Learning in Robots.- I: Biologically Inspired Models for Motor Learning.- Distributed Adaptive Control: A Proposal on the Neuronal Organization of Adaptive Goal Oriented Behavior.- Proprioception and Imitation: On the Road to Agent Individuation.- Adaptive Optimal Feedback Control with Learned Internal Dynamics Models.- The SURE_REACH Model for Motor Learning and Control of a Redundant Arm: From Modeling Human Behavior to Applications in Robotics.- Intrinsically Motivated Exploration for Developmental and Active Sensorimotor Learning.- II: Learning Policies for Motor Control.- Learning to Exploit Proximal Force Sensing: A Comparison Approach.- Learning Forward Models for the Operational Space Control of Redundant Robots.- Real-Time Local GP Model Learning.- Imitation and Reinforcement Learning for Motor Primitives with Perceptual Coupling.- A Bayesian View on Motor Control and Planning.- Methods for Learning Control Policies from Variable-Constraint Demonstrations.- Motor Learning at Intermediate Reynolds Number: Experiments with Policy Gradient on the Flapping Flight of a Rigid Wing.- III: Imitation and Interaction Learning.- Abstraction Levels for Robotic Imitation: Overview and Computational Approaches.- Learning to Imitate Human Actions through Eigenposes.- Incremental Learning of Full Body Motion Primitives.- Can We Learn Finite State Machine Robot Controllers from Interactive Demonstration?.- Mobile Robot Motion Control from Demonstration and Corrective Feedback.- Learning Continuous Grasp Affordances by Sensorimotor Exploration.- Multimodal Language Acquisition Based on Motor Learning and Interaction.- Human-Robot Cooperation Based on Interaction Learning.