Demonstration-guided Optimal Control for Long-term Non-prehensile Planar Manipulation

TL;DR

This paper introduces a demonstration-guided hierarchical optimization framework for long-term non-prehensile planar manipulation, effectively handling contact uncertainties and hybrid control to improve robot planning and control.

Contribution

It extends the dynamics model of the pusher-slider system to include separation modes and uses human demonstrations for better task and motion planning.

Findings

Successfully copes with local minima in optimization.

Validates approach in simulation and real robot experiments.

Demonstrates robustness to external disturbances.

Abstract

Long-term non-prehensile planar manipulation is a challenging task for robot planning and feedback control. It is characterized by underactuation, hybrid control, and contact uncertainty. One main difficulty is to determine both the continuous and discrete contact configurations, e.g., contact points and modes, which requires joint logical and geometrical reasoning. To tackle this issue, we propose a demonstration-guided hierarchical optimization framework to achieve offline task and motion planning (TAMP). Our work extends the formulation of the dynamics model of the pusher-slider system to include separation mode with face switching mechanism, and solves a warm-started TAMP problem by exploiting human demonstrations. We show that our approach can cope well with the local minima problems currently present in the state-of-the-art solvers and determine a valid solution to the task. We validate our results in simulation and demonstrate its applicability on a pusher-slider system with a real Franka Emika robot in the presence of external disturbances.