Body Schema Acquisition through Active Learning

Abstract

We present an active learning algorithm for the problem of body schema learning, i.e. estimating a kinematic model of a serial robot. The learning process is done online using Recursive Least Squares (RLS) estimation, which outperforms gradient methods usually applied in the literature. In addiction, the method provides the required information to apply an active learning algorithm to find the optimal set of robot configurations and observations to improve the learning process. By selecting the most informative observations, the proposed method minimizes the required amount of data. We have developed an efficient version of the active learning algorithm to select the points in real-time. The algorithms have been tested and compared using both simulated environments and a real humanoid robot.

arXiv commentInternational Conference on Robotics and Automation (ICRA) 2010

References in corpus (1)

• PEGASUS: A Policy Search Method for Large MDPs and POMDPs

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• Active Learning for Autonomous Intelligent Agents: Exploration, Curiosity, and Interaction
• Bayesian Body Schema Estimation using Tactile Information obtained through Coordinated Random Movements
• Online Body Schema Adaptation through Cost-Sensitive Active Learning