Behavior recognition for learning from demonstration
2010 (English)In: Proceedings of IEEE International Conference on Robotics and Automation / [ed] Nancy M. Amato et. al, 2010, 866-872 p.Conference paper (Refereed)
Two methods for behavior recognition are presented and evaluated. Both methods are based on the dynamic temporal difference algorithm Predictive Sequence Learning (PSL) which has previously been proposed as a learning algorithm for robot control. One strength of the proposed recognition methods is that the model PSL builds to recognize behaviors is identical to that used for control, implying that the controller (inverse model) and the recognition algorithm (forward model) can be implemented as two aspects of the same model. The two proposed methods, PSLE-Comparison and PSLH-Comparison, are evaluated in a Learning from Demonstration setting, where each algorithm should recognize a known skill in a demonstration performed via teleoperation. PSLH-Comparison produced the smallest recognition error. The results indicate that PSLH-Comparison could be a suitable algorithm for integration in a hierarchical control system consistent with recent models of human perception and motor control.
Place, publisher, year, edition, pages
2010. 866-872 p.
learning and adaptive systems, neurorobotics, autonomous agents
Research subject Computer Science
IdentifiersURN: urn:nbn:se:umu:diva-32375ISBN: 978-1-4244-5040-4OAI: oai:DiVA.org:umu-32375DiVA: diva2:303023
ICRA 2010 IEEE International conference on robotics and automationce, Anchorage, Alaska, May 3-8, 2010