Artificial Intelligence Review - Special issue on lazy learning
Robot Learning From Demonstration
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Apprenticeship learning via inverse reinforcement learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Teaching and Working with Robots as a Collaboration
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Interactive robot task training through dialog and demonstration
Proceedings of the ACM/IEEE international conference on Human-robot interaction
Learning by demonstration with critique from a human teacher
Proceedings of the ACM/IEEE international conference on Human-robot interaction
A survey of robot learning from demonstration
Robotics and Autonomous Systems
Observer-based dynamic walking control for biped robots
Robotics and Autonomous Systems
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Interactive policy learning through confidence-based autonomy
Journal of Artificial Intelligence Research
Mechatronic design of NAO humanoid
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Dynamical System Modulation for Robot Learning via Kinesthetic Demonstrations
IEEE Transactions on Robotics
Human behavior understanding for robotics
HBU'12 Proceedings of the Third international conference on Human Behavior Understanding
Hi-index | 0.00 |
We contribute a method for improving the skill execution performance of a robot by complementing an existing algorithmic solution with corrective human demonstration. We apply the proposed method to the biped walking problem, which is a good example of a complex low level skill due to the complicated dynamics of the walk process in a high dimensional state and action space. We introduce an incremental learning approach to improve the Nao humanoid robot's stability during walking. First, we identify, extract, and record a complete walk cycle from the motion of the robot as it executes a given walk algorithm as a black box. Second, we apply offline advice operators for improving the stability of the learned open-loop walk cycle. Finally, we present an algorithm to directly modify the recorded walk cycle using real time corrective human demonstration. The demonstrator delivers the corrective feedback using a commercially available wireless game controller without touching the robot. Through the proposed algorithm, the robot learns a closed-loop correction policy for the open-loop walk by mapping the corrective demonstrations to the sensory readings received while walking. Experiment results demonstrate a significant improvement in the walk stability.