# Planning and Resilient Execution of Policies For Manipulation in Contact with Actuation Uncertainty

## TL;DR

This paper introduces a two-stage method for robot manipulation planning and execution that accounts for actuation uncertainty, contact, and compliance, demonstrating resilience to environmental changes in simulation and real robot tests.

## Contribution

It presents a novel sampling-based planning approach combined with resilient policy adaptation for manipulation under uncertainty and contact conditions.

## Key findings

- Efficient policy generation for contact-rich manipulation tasks.
- Resilient policy execution adapts to environmental changes like new obstacles.
- Method outperforms simpler approaches in simulated environments.

## Abstract

We propose a method for planning motion for robots with actuation uncertainty that incorporates contact with the environment and the compliance of the robot to reliably perform manipulation tasks. Our approach consists of two stages: (1) Generating partial policies using a sampling-based motion planner that uses particle-based models of uncertainty and simulation of contact and compliance; and (2) Resilient execution that updates the planned policies to account for unexpected behavior in execution which may arise from model or environment inaccuracy. We have tested our planner and policy execution in simulated SE(2) and SE(3) environments and Baxter robot. We show that our methods efficiently generate policies to perform manipulation tasks involving significant contact and compare against several simpler methods. Additionally, we show that our policy adaptation is resilient to significant changes during execution; e.g. adding a new obstacle to the environment.