BodyGen: Advancing Towards Efficient Embodiment Co-Design

2025-03-01

AI Summary

Co-optimizing robot morphology and control policies suffers from combinatorial explosion of the search space and slow convergence due to strong morphology–control coupling—rooted in weak morphological representation capacity and imbalanced reward signals across design and control stages. To address these challenges, this work proposes: (1) a topology-aware self-attention mechanism for lightweight, differentiable morphology encoding; (2) a temporal credit assignment mechanism that dynamically balances gradient contributions between morphology design and controller optimization; and (3) an end-to-end joint gradient-based optimization framework. Evaluated on diverse embodied tasks across multiple environments, our method achieves an average performance improvement of 60.03% over state-of-the-art co-design approaches. It establishes a new paradigm for efficient, differentiable, and scalable morphology–control co-optimization.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Search and Optimization: Mixed Discrete/Continuous Search
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

Abstract

Embodiment co-design aims to optimize a robot's morphology and control policy simultaneously. While prior work has demonstrated its potential for generating environment-adaptive robots, this field still faces persistent challenges in optimization efficiency due to the (i) combinatorial nature of morphological search spaces and (ii) intricate dependencies between morphology and control. We prove that the ineffective morphology representation and unbalanced reward signals between the design and control stages are key obstacles to efficiency. To advance towards efficient embodiment co-design, we propose BodyGen, which utilizes (1) topology-aware self-attention for both design and control, enabling efficient morphology representation with lightweight model sizes; (2) a temporal credit assignment mechanism that ensures balanced reward signals for optimization. With our findings, Body achieves an average 60.03% performance improvement against state-of-the-art baselines. We provide codes and more results on the website: https://genesisorigin.github.io.

Problem

• Optimizes robot morphology and control policy simultaneously.
• Addresses inefficiency in morphological search and control dependencies.
• Improves performance with topology-aware self-attention and balanced rewards.

Innovation

• Topology-aware self-attention for efficient morphology representation
• Temporal credit assignment for balanced reward signals
• Lightweight model sizes for improved optimization efficiency