Skill-CPD: Real-time Skill Refinement for Shared Autonomy in Manipulator Teleoperation

Advanced wireless communication networks provide lower latency and a higher transmission rate. Although this is an enabler for many new teleoperation applications, the risk of network instability or packet drop is still unavoidable. Real-time manipulator teleoperation requires data transmission with no discontinuity. Shared autonomy (SA) is a standard method to mitigate this issue. In this way, if the data from the remote side is unavailable, the controller can continue based on the previously observed models. However, due to the spatial gap between human and robot trajectories, indisputable fluctuations occur, which cause issues in teleoperation applications. This motivates us to propose a new skill refinement strategy to modify the previously trained skill and mitigate the sudden unwanted motions within the control takeover phase. To this end, our approach comprises applying the Hidden Semi-Markov Model (HSMM) and Linear Quadratic Tracker (LQT) in combination to learn and predict the user's intentions and then exploiting Coherent Point Drift (CPD) to refine the executable trajectory. We test our method both in simulation and in the real world for 2D English letter drawing and 3D robot-assisted feeding scenarios. Our experimental results using the Kinova® Movo platform show that the proposed refinement approach generates a stable trajectory and mitigates the control switching inconsistency. All comprehensive experiments and source code is available at: http://cxdcxd.github.io/SkillCPD.