Rebalance Strategies for Humanoids Walking by Foot Positioning Compensator Based on Adaptive Heteroscedastic SpGPs

摘要：

To solve the rebalance problem of a full-body humanoid walking, an adaptive foot positioning compensation approach is proposed. To obtain a more precise initial policy, a constrained dynamics model is used to generate the offline policy. A heteroscedastic sparse Gaussian process is applied for online calculation of the foot positioning policy. In order to make the generated policy to adapt with the full-body dynamics, a sample-efficient MAP-like updating method for the heteroscedastic sparse Gaussian process model is also proposed. Experiments on both simulation and a real full-body humanoid are developed to show the performance of the final foot positioning policy. With the help of proposed method, the full-body humanoid robot succeeded walking down an elastic deformable platform and several obvious compensation foot steps can be observed for the robot to retrieve its balance.

作者: XU Tao CHEN Qijun CAI Zhiqiang

作者单位: School of Electronics and Information,Tongji University

会议类型: 国际会议

会议名称: 2011 IEEE International Conference on Robotics and Automation(2011年IEEE世界机器人与自动化大会 ICRA 2011)

会议地点: 上海

会议语种:英文

页码: 563-568

在线出版日期: 2011-05-09（万方平台首次上网日期，不代表论文的发表时间）