Local Trajectory Stabilization for Dexterous Manipulation via Piecewise Affine Approximations
This addresses the challenge of robust control for dexterous manipulation in robotics, representing an incremental improvement with specific gains in stability.
The paper tackles the problem of designing feedback policies for dexterous robotic manipulation by proposing a model-based approach that uses piecewise affine approximations to stabilize trajectories, and validates it on hardware, showing the controller can accomplish tasks even under large external disturbances.
We propose a model-based approach to design feedback policies for dexterous robotic manipulation. The manipulation problem is formulated as reaching the target region from an initial state for some non-smooth nonlinear system. First, we use trajectory optimization to find a feasible trajectory. Next, we characterize the local multi-contact dynamics around the trajectory as a piecewise affine system, and build a funnel around the linearization of the nominal trajectory using polytopes. We prove that the feedback controller at the vicinity of the linearization is guaranteed to drive the nonlinear system to the target region. During online execution, we solve linear programs to track the system trajectory. We validate the algorithm on hardware, showing that even under large external disturbances, the controller is able to accomplish the task.