N-LIMB: Neural Limb Optimization for Efficient Morphological Design

TL;DR

N-LIMB introduces a universal control policy that efficiently optimizes robot morphology and control simultaneously, enabling rapid discovery of high-performing designs across diverse terrains.

Contribution

The paper presents a novel framework combining a universal control policy with design optimization, improving sample efficiency and enabling joint morphology and control optimization.

Findings

Successfully optimized diverse robot morphologies for locomotion tasks.

Discovered novel high-performing design-control pairs.

Demonstrated effectiveness across multiple terrains.

Abstract

A robot's ability to complete a task is heavily dependent on its physical design. However, identifying an optimal physical design and its corresponding control policy is inherently challenging. The freedom to choose the number of links, their type, and how they are connected results in a combinatorial design space, and the evaluation of any design in that space requires deriving its optimal controller. In this work, we present N-LIMB, an efficient approach to optimizing the design and control of a robot over large sets of morphologies. Central to our framework is a universal, design-conditioned control policy capable of controlling a diverse sets of designs. This policy greatly improves the sample efficiency of our approach by allowing the transfer of experience across designs and reducing the cost to evaluate new designs. We train this policy to maximize expected return over a distribution of designs, which is simultaneously updated towards higher performing designs under the universal policy. In this way, our approach converges towards a design distribution peaked around high-performing designs and a controller that is effectively fine-tuned for those designs. We demonstrate the potential of our approach on a series of locomotion tasks across varying terrains and show the discovery novel and high-performing design-control pairs.