Property-Aware Robot Object Manipulation: a Generative Approach

TL;DR

This paper presents a generative approach using GANs to produce robot manipulation motions that intuitively convey object properties like weight and fragility, enhancing human-robot collaboration.

Contribution

It introduces a novel method leveraging GANs to generate adaptive robot actions based on object properties without extensive demonstration data.

Findings

GANs can generate meaningful, property-aware robot motions

Generated actions effectively reflect object weight and fragility

The approach reduces the need for large demonstration datasets

Abstract

When transporting an object, we unconsciously adapt our movement to its properties, for instance by slowing down when the item is fragile. The most relevant features of an object are immediately revealed to a human observer by the way the handling occurs, without any need for verbal description. It would greatly facilitate collaboration to enable humanoid robots to perform movements that convey similar intuitive cues to the observers. In this work, we focus on how to generate robot motion adapted to the hidden properties of the manipulated objects, such as their weight and fragility. We explore the possibility of leveraging Generative Adversarial Networks to synthesize new actions coherent with the properties of the object. The use of a generative approach allows us to create new and consistent motion patterns, without the need of collecting a large number of recorded human-led demonstrations. Besides, the informative content of the actions is preserved. Our results show that Generative Adversarial Nets can be a powerful tool for the generation of novel and meaningful transportation actions, which result effectively modulated as a function of the object weight and the carefulness required in its handling.