Learning robust manipulation tasks involving contact using trajectory parameterized probabilistic principal component analysis

In this paper, we aim to expedite the deployment of challenging manipulation tasks involving both motion and contact wrenches (forces and moments). To this end, we acquire motion and wrench signals from a small set of demonstrations using passive observation. To learn these tasks, we introduce Trajectory parameterized Probabilistic Principal Component Analysis (traPPCA) which compactly re-parameterizes the acquired signals using trajectory information and encodes the signal correlations using Probabilistic Principal Component Analysis (PPCA). Finally, the task is transferred to a robot setup by specifying the robot behavior using a constraint-based task specification and control approach. This framework results in increased robustness of the system against different sources of uncertainty: imprecise sensors, tool adaptation, and changes in the execution speed.

ISBN:978-1-7281-6212-6

Publication year:2021

Accessibility:Closed