Debate2Create: Robot Co-design via Large Language Model Debates

2025-10-29

AI Summary

Robot morphology and control co-design faces challenges of an exponentially large search space and strong morphology–behavior coupling. Method: This paper proposes a structured dialectical debate framework powered by large language model (LLM)-based agents: a morphology agent generates structural improvements; a control agent concurrently customizes reward functions; and a diverse panel of evaluators assesses designs via physics simulation, providing feedback to drive closed-loop iterative optimization. Contribution/Results: To our knowledge, this is the first work to introduce multi-agent dialectical reasoning into robotic co-evolution—enabling spontaneous emergence of diverse, specialized morphologies without explicit diversity constraints. Evaluated on quadrupedal locomotion, the resulting robots achieve a 73% average improvement in forward travel distance over baseline methods, demonstrating both efficacy and novelty in automated robot design.

Technology Category

• Intelligent Robots: Behavior Learning & Control
• Multiagent Systems: Adversarial Agents
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search

Abstract

Automating the co-design of a robot's morphology and control is a long-standing challenge due to the vast design space and the tight coupling between body and behavior. We introduce Debate2Create (D2C), a framework in which large language model (LLM) agents engage in a structured dialectical debate to jointly optimize a robot's design and its reward function. In each round, a design agent proposes targeted morphological modifications, and a control agent devises a reward function tailored to exploit the new design. A panel of pluralistic judges then evaluates the design-control pair in simulation and provides feedback that guides the next round of debate. Through iterative debates, the agents progressively refine their proposals, producing increasingly effective robot designs. Notably, D2C yields diverse and specialized morphologies despite no explicit diversity objective. On a quadruped locomotion benchmark, D2C discovers designs that travel 73% farther than the default, demonstrating that structured LLM-based debate can serve as a powerful mechanism for emergent robot co-design. Our results suggest that multi-agent debate, when coupled with physics-grounded feedback, is a promising new paradigm for automated robot design.

Problem

• Automating robot morphology and control co-design due to vast search space
• Addressing tight coupling between robot body design and behavior control
• Optimizing robot designs without explicit diversity objectives through structured debates

Innovation

• LLM agents debate to optimize robot design
• Iterative rounds refine morphology and control
• Physics simulation feedback guides design evolution