Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine learning for fast quadrupedal locomotion
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
A model-based approach to robot joint control
RoboCup 2004
MARIOnET: motion acquisition for robots through iterative online evaluative training
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Integrating reinforcement learning with human demonstrations of varying ability
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Portable autonomous walk calibration for 4-legged robots
Applied Intelligence
Strategy-Based learning through communication with humans
KES-AMSTA'12 Proceedings of the 6th KES international conference on Agent and Multi-Agent Systems: technologies and applications
Humanoid robots learning to walk faster: from the real world to simulation and back
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
A comparison between a communication-based and a data mining-based learning approach for agents
Intelligent Decision Technologies
Hi-index | 0.00 |
A fast gait is an essential component of any successful team in the RoboCup 4-legged league. However, quickly moving quadruped robots, including those with learned gaits, often move in such a way so as to cause unsteady camera motions which degrade the robot's visual capabilities. This paper presents an implementation of the policy gradient machine learning algorithm that searches for a parameterized walk while optimizing for both speed and stability. To the best of our knowledge, previous learned walks have all focused exclusively on speed. Our method is fully implemented and tested on the Sony Aibo ERS-7 robot platform. The resulting gait is reasonably fast and considerably more stable compared to our previous fast gaits. We demonstrate that this stability can significantly improve the robot's visual object recognition.