A Neural Network Method of Learning Human Motion by Observation in Operational Space

A Spiers, G Herrmann, CR Melhuish, AG Pipe

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

Abstract

Learning by observation is a useful goal for humanoid robots that strive for human-like motion. Over time the movements and actions of these robots are likely to require modification for different scenarios and environments. Therefore being able to ‘teach’ the robot new tasks and motions without tedious programming is highly useful. The operational space formulation is a highly elegant robot manipulator control method that has parallels with human motion strategies, with prioritization of task achievement and redundant degrees of freedom control via effort optimisation. In this paper, we combine a novel method of learning by observation with the operational space framework. This has created a learning controller that is able to generalise a small set of example reaching motions in order to reach to new targets while maintaining human like motion rajectories in the posture space via a novel sliding mode optimal controller. The trajectories are executed on a (dynamically) simulated robot arm which performs human like reaching to arbitrary locations. Trajectories are minimally encoded as the coefficients of fitted polynomials and the learning is performed by three neural networks that learn and generalise. Initial results are presented for this early stage of research on this proposed framework.
Translated title of the contributionA Neural Network Method of Learning Human Motion by Observation in Operational Space
Original languageEnglish
Title of host publication2010 IEEE-RAS International Conference on Humanoid Robots
DOIs
Publication statusPublished - 2010

Bibliographical note

Conference Organiser: IEEE Robotics and Automation Society

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