Iterative Path Optimisation For Personalised Dressing Assistance Using Vision And Force Information
2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)(2016)
摘要
We propose an online iterative path optimisation method to enable a Baxter humanoid robot to assist human users to dress. The robot searches for the optimal personalised dressing path using vision and force sensor information: vision information is used to recognise the human pose and model the movement space of upper-body joints; force sensor information is used for the robot to detect external force resistance and to locally adjust its motion. We propose a new stochastic path optimisation method based on adaptive moment estimation. We first compare the proposed method with other path optimisation algorithms on synthetic data. Experimental results show that the performance of the method achieves the smallest error with fewer iterations and less computation time. We also evaluate real-world data by enabling the Baxter robot to assist real human users with their dressing.
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关键词
synthetic data,adaptive moment estimation,stochastic path optimisation,external force resistance,upper-body joints,movement space,human pose recognition,force sensor information,vision sensor information,Baxter humanoid robot,online iterative path optimisation,force information,personalised dressing assistance
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