Is Linear Feedback on Smoothed Dynamics Sufficient for Stabilizing Contact-Rich Plans?
This work addresses the problem of robust control in contact-rich manipulation for robotics, but it is incremental as it builds on existing contact smoothing methods.
The paper tackled the challenge of stabilizing contact-rich manipulation plans by analyzing linear controller synthesis on smoothed contact dynamics, finding that LQR was insufficient for stabilizing over 300 trajectories in bimanual whole-body manipulation experiments.
Designing planners and controllers for contact-rich manipulation is extremely challenging as contact violates the smoothness conditions that many gradient-based controller synthesis tools assume. Contact smoothing approximates a non-smooth system with a smooth one, allowing one to use these synthesis tools more effectively. However, applying classical control synthesis methods to smoothed contact dynamics remains relatively under-explored. This paper analyzes the efficacy of linear controller synthesis using differential simulators based on contact smoothing. We introduce natural baselines for leveraging contact smoothing to compute (a) open-loop plans robust to uncertain conditions and/or dynamics, and (b) feedback gains to stabilize around open-loop plans. Using robotic bimanual whole-body manipulation as a testbed, we perform extensive empirical experiments on over 300 trajectories and analyze why LQR seems insufficient for stabilizing contact-rich plans. The video summarizing this paper and hardware experiments is found here: https://youtu.be/HLaKi6qbwQg?si=_zCAmBBD6rGSitm9.