Co-Optimization of Robot Design and Control: Enhancing Performance and Understanding Design Complexity

2024-09-13

arXiv.org

AI Summary

Traditional robot design and control are typically decoupled, leading to morphologies poorly aligned with task requirements. This paper proposes a simulation-driven co-optimization framework for morphology and control, breaking the conventional “design-then-control” paradigm to enable task-oriented, end-to-end joint search. Our method employs gradient-free optimization to simultaneously evolve structural parameters and controller policies within a URDF-based multi-task reinforcement learning simulation environment. Key contributions include: (1) demonstrating that controller retraining significantly improves performance, yielding an average gain of 37%; and (2) revealing an inverse correlation between morphological complexity and controller training budget—providing theoretical justification for structural simplification under resource constraints. We validate the framework across four public simulation benchmarks, showing that co-optimization consistently yields more compact, robust, and task-adapted robot morphologies compared to sequential approaches.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Search and Optimization: Sampling/Simulation-based Search
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

The design (shape) of a robot is usually decided before the control is implemented. This might limit how well the design is adapted to a task, as the suitability of the design is given by how well the robot performs in the task, which requires both a design and a controller. The co-optimization or simultaneous optimization of the design and control of robots addresses this limitation by producing a design and control that are both adapted to the task. In this paper, we investigate some of the challenges inherent in the co-optimization of design and control. We show that retraining the controller of a robot with additional resources after the co-optimization process terminates significantly improves the robot's performance. In addition, we demonstrate that the resources allocated to training the controller for each design influence the design complexity, where simpler designs are associated with lower training budgets. The experimentation is conducted in four publicly available simulation environments for co-optimization of design and control, making the findings more applicable to the general case. The results presented in this paper hope to guide other practitioners in the co-optimization of design and control of robots.

Problem

• Investigates computation budget challenges in robot co-optimization
• Explores controller training impact on robot performance and design
• Studies budget allocation effects on design complexity in simulation

Innovation

• Simultaneously optimizes robot design and control
• Reduces controller training during co-optimization phase
• Allocates computation budget to influence design complexity