http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
From Motor Learning to Interaction Learning in Robots
From an engineering standpoint, the increasing complexity of robotic systems and the increasing demand for more autonomously learning robots, has become essential. This book is largely based on the successful workshop “From motor to interaction learning in robots” held at the IEEE/RSJ International Conference on Intelligent Robot Systems. The major aim of the book is to give students interested the topics described above a chance to get started faster and researchers a helpful compandium.
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.
Artificial Intelligence (incl. Robotics)
Studies in Computational Intelligence, 264