Sequence-of-Constraints MPC: Reactive Timing-Optimal Control of Sequential Manipulation

TL;DR

This paper introduces a reactive, timing-optimal control method for sequential manipulation tasks, bridging the gap between task planning and control for robust, real-time execution of complex manipulation sequences.

Contribution

It proposes a novel model predictive control approach that decomposes the control problem into sub-problems for online solving, enabling reactive, timing-aware execution of TAMP plans.

Findings

Effective reactive control for sequential manipulation demonstrated.

Robustness to perturbations through phase backtracking.

Timing-optimal transitions between constraints achieved.

Abstract

Task and Motion Planning has made great progress in solving hard sequential manipulation problems. However, a gap between such planning formulations and control methods for reactive execution remains. In this paper we propose a model predictive control approach dedicated to robustly execute a single sequence of constraints, which corresponds to a discrete decision sequence of a TAMP plan. We decompose the overall control problem into three sub-problems (solving for sequential waypoints, their timing, and a short receding horizon path) that each is a non-linear program solved online in each MPC cycle. The resulting control strategy can account for long-term interdependencies of constraints and reactively plan for a timing-optimal transition through all constraints. We additionally propose phase backtracking when running constraints of the current phase cannot be fulfilled, leading to a fluent re-initiation behavior that is robust to perturbations and interferences by an experimenter.