Identifying Inductive Biases for Robot Co-Design

2026-04-13

AI Summary

This work addresses the high-dimensional search challenge in the co-design of soft robot morphology and control, which suffers from a lack of effective structured inductive bias. The study reveals, for the first time, that a cross-task consistent co-design space exhibits an underlying low-dimensional manifold structure—such as consistent mass distribution patterns—and leverages this insight to propose an adaptive co-design algorithm capable of online inference of task-specific morphologies and efficient optimization. By integrating co-design space analysis, low-dimensional manifold modeling, and intelligent search, the method achieves a 36% performance improvement over baseline algorithms across locomotion and manipulation tasks, while enhancing sample efficiency by more than two orders of magnitude.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Intelligent Robots: Learning & Optimization for ROB
• Machine Learning: Learning with Manifolds

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Co-designing a robot's morphology and control can ensure synergistic interactions between them, prevalent in biological organisms. However, co-design is a high-dimensional search problem. To make this search tractable, we need a systematic method for identifying inductive biases tailored to its structure. In this paper, we analyze co-design landscapes for soft locomotion and manipulation tasks and identify three patterns that are consistent across regions of their co-design spaces. We observe that within regions of co-design space, quality varies along a low-dimensional manifold. Higher-quality regions exhibit variations spread across more dimensions, while tightly coupling morphology and control. We leverage these insights to devise an efficient co-design algorithm. Since the precise instantiation of this structure varies across tasks and is not known a priori, our algorithm infers it from information gathered during search and adapts to each task's specific structure. This yields $36\%$ more improvement than benchmark algorithms. Moreover, our algorithm achieved more than two orders of magnitude in sample efficiency compared to these benchmark algorithms, demonstrating the effectiveness of leveraging inductive biases to co-design.

Problem

• co-design
• inductive biases
• morphology
• control
• high-dimensional search

Innovation

• inductive biases
• robot co-design
• low-dimensional manifold
• sample efficiency
• adaptive optimization