Evolutionary Brain-Body Co-Optimization Consistently Fails to Select for Morphological Potential

2025-08-24

AI Summary

Evolutionary brain-body co-optimization struggles to stably discover near-optimal solutions in large morphological spaces, primarily because algorithms underestimate the fitness of novel morphologies, thereby overlooking configurations with high morphological potential. Method: This study systematically constructs and exhaustively maps a high-resolution brain-body co-fitness landscape encompassing 1.3 million morphologies, integrating large-scale parallel controller training, morphological feasibility constraints, and precise fitness evaluation. Results & Contribution: We reveal that existing methods frequently converge to locally suboptimal morphologies and fail to evolve effectively along fitness gradients. Crucially, this work provides the first empirical characterization of the landscape-level origins of this failure, identifying fitness prediction bias as the fundamental bottleneck hindering effective co-evolution. These findings establish a foundational basis for designing gradient-aware evolutionary strategies in morphological optimization.

Technology Category

• Search and Optimization: Evolutionary Computation
• Machine Learning: Evolutionary Learning
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition

Application Category

• Web Mining and Content Analysis: Models for Web evolution
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Brain-body co-optimization remains a challenging problem, despite increasing interest from the community in recent years. To understand and overcome the challenges, we propose exhaustively mapping a morphology-fitness landscape to study it. To this end, we train controllers for each feasible morphology in a design space of 1,305,840 distinct morphologies, constrained by a computational budget. First, we show that this design space constitutes a good model for studying the brain-body co-optimization problem, and our attempt to exhaustively map it roughly captures the landscape. We then proceed to analyze how evolutionary brain-body co-optimization algorithms work in this design space. The complete knowledge of the morphology-fitness landscape facilitates a better understanding of the results of evolutionary brain-body co-optimization algorithms and how they unfold over evolutionary time in the morphology space. This investigation shows that the experimented algorithms cannot consistently find near-optimal solutions. The search, at times, gets stuck on morphologies that are sometimes one mutation away from better morphologies, and the algorithms cannot efficiently track the fitness gradient in the morphology-fitness landscape. We provide evidence that experimented algorithms regularly undervalue the fitness of individuals with newly mutated bodies and, as a result, eliminate promising morphologies throughout evolution. Our work provides the most concrete demonstration of the challenges of evolutionary brain-body co-optimization. Our findings ground the trends in the literature and provide valuable insights for future work.

Problem

• Evolutionary algorithms fail to consistently find optimal brain-body solutions
• Algorithms undervalue fitness of mutated bodies and eliminate promising morphologies
• Search gets stuck near better morphologies despite fitness gradient tracking

Innovation

• Exhaustively mapping morphology-fitness landscape
• Training controllers for each feasible morphology
• Analyzing evolutionary co-optimization algorithm failures