What Robot do I Need? Fast Co-Adaptation of Morphology and Control using Graph Neural Networks

TL;DR

This paper introduces a novel method combining neural networks and graph neural networks for efficient co-adaptation of robot morphology and control, addressing the simulation-to-reality gap and reducing manufacturing cycles.

Contribution

It presents a new data-efficient co-adaptation approach that integrates high-frequency neural networks with graph neural networks for robots with varying degrees of freedom.

Findings

Efficient co-adaptation within limited production cycles

Combines design optimization with offline reinforcement learning

Potential for direct real-world application

Abstract

The co-adaptation of robot morphology and behaviour becomes increasingly important with the advent of fast 3D-manufacturing methods and efficient deep reinforcement learning algorithms. A major challenge for the application of co-adaptation methods to the real world is the simulation-to-reality-gap due to model and simulation inaccuracies. However, prior work focuses primarily on the study of evolutionary adaptation of morphologies exploiting analytical models and (differentiable) simulators with large population sizes, neglecting the existence of the simulation-to-reality-gap and the cost of manufacturing cycles in the real world. This paper presents a new approach combining classic high-frequency deep neural networks with computational expensive Graph Neural Networks for the data-efficient co-adaptation of agents with varying numbers of degrees-of-freedom. Evaluations in simulation show that the new method can co-adapt agents within such a limited number of production cycles by efficiently combining design optimization with offline reinforcement learning, that it allows for the direct application to real-world co-adaptation tasks in future work