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The Shortcomings of Force-from-Motion in Robot Learning

Elie Aljalbout, Felix Frank, Patrick van der Smagt, Alexandros Paraschos

TL;DR

The paper addresses the limitation of force-from-motion for robotic manipulation, arguing that motion-centric action spaces inadequately constrain interaction forces and hinder general-purpose manipulation. It analyzes these shortcomings through a 1D pushing example and surveys alternative action spaces, emphasizing interaction-explicit representations as a path to more robust control. The findings show that force-from-motion can necessitate impractically high stiffness or lead to unsafe or non-compliant behavior, while other spaces trade learning efficiency and safety in different ways but promise improved contact control. The work highlights the potential of interaction-explicit action spaces to enhance generalization and real-world applicability, and calls for future research to develop trainable, imitation-friendly interaction-based policies.

Abstract

Robotic manipulation requires accurate motion and physical interaction control. However, current robot learning approaches focus on motion-centric action spaces that do not explicitly give the policy control over the interaction. In this paper, we discuss the repercussions of this choice and argue for more interaction-explicit action spaces in robot learning.

The Shortcomings of Force-from-Motion in Robot Learning

TL;DR

The paper addresses the limitation of force-from-motion for robotic manipulation, arguing that motion-centric action spaces inadequately constrain interaction forces and hinder general-purpose manipulation. It analyzes these shortcomings through a 1D pushing example and surveys alternative action spaces, emphasizing interaction-explicit representations as a path to more robust control. The findings show that force-from-motion can necessitate impractically high stiffness or lead to unsafe or non-compliant behavior, while other spaces trade learning efficiency and safety in different ways but promise improved contact control. The work highlights the potential of interaction-explicit action spaces to enhance generalization and real-world applicability, and calls for future research to develop trainable, imitation-friendly interaction-based policies.

Abstract

Robotic manipulation requires accurate motion and physical interaction control. However, current robot learning approaches focus on motion-centric action spaces that do not explicitly give the policy control over the interaction. In this paper, we discuss the repercussions of this choice and argue for more interaction-explicit action spaces in robot learning.
Paper Structure (3 sections, 2 equations, 1 figure)

This paper contains 3 sections, 2 equations, 1 figure.

Table of Contents

  1. Introduction
  2. Shortcomings of force-from-motion
  3. Discussion

Figures (1)

  • Figure 1: A 1-dimensional (1D) manipulation example. The task is to push the blue cube to the target. The robot can move in the range $[q_\mathrm{min}, q_\mathrm{max}]$. The exerted force $F$, generated by the policy and the low-level controller, has to overcome the fiction $F_\mathrm{fric}$ for the cube to move. Using a motion-centric action space, such as joint positions, the robot can only apply forces indirectly by motion commands. That is by setting the low-level controller's target further away than the actual target position. This approach has several shortcomings, as discussed in Sec. \ref{['sec:shortcomings']} and the use of an interaction-explicit action space overcomes them.