Robust Planning for Multi-stage Forceful Manipulation

TL;DR

This paper introduces a planning framework for multi-stage forceful manipulation tasks that explicitly considers physical constraints like torque and friction, enabling robots to select robust strategies in complex scenarios.

Contribution

It extends task and motion planning with force-aware constraints and cost-sensitive optimization, improving robustness in forceful manipulation tasks.

Findings

Successfully applied to bottle opening, nut twisting, and vegetable cutting

System selects strategies considering physical constraints and uncertainties

Demonstrates improved robustness and strategy selection in complex tasks

Abstract

Multi-step forceful manipulation tasks, such as opening a push-and-twist childproof bottle, require a robot to make various planning choices that are substantially impacted by the requirement to exert force during the task. The robot must reason over discrete and continuous choices relating to the sequence of actions, such as whether to pick up an object, and the parameters of each of those actions, such how to grasp the object. To enable planning and executing forceful manipulation, we augment an existing task and motion planner with constraints that explicitly consider torque and frictional limits, captured through the proposed forceful kinematic chain constraint. In three domains, opening a childproof bottle, twisting a nut and cutting a vegetable, we demonstrate how the system selects from among a combinatorial set of strategies.We also show how cost-sensitive planning can be used to find strategies and parameters that are robust to uncertainty in the physical parameters.