Robots that redesign themselves through kinematic self-destruction

2026-03-12

AI Summary

Traditional robots rely on predefined morphologies and struggle to autonomously optimize their body structures during operation to adapt to tasks. This work proposes a self-design approach based on a kinematic self-destruction mechanism: the robot uses proprioception to identify redundant limbs, actively severs them, and dynamically reconfigures its morphology to enhance locomotion performance. For the first time, this method enables a robot to participate in its own structural design through irreversible self-destruction within its operational lifetime. A single autoregressive sequence model serves as a universal controller, trained in simulation to learn when and how to simplify its body and subsequently control the resulting new configuration. The learned policy successfully transfers to real-world platforms and unseen morphologies, achieving significantly superior forward locomotion compared to baselines that either randomly remove limbs or retain the original structure, thereby demonstrating the feasibility and advantages of self-designing robots.

Technology Category

• Intelligent Robots: Manipulation
• Search and Optimization: Learning to Search
• Machine Learning: Adversarial Learning & Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Every robot built to date was predesigned by an external process, prior to deployment. Here we show a robot that actively participates in its own design during its lifetime. Starting from a randomly assembled body, and using only proprioceptive feedback, the robot dynamically ``sculpts'' itself into a new design through kinematic self-destruction: identifying redundant links within its body that inhibit its locomotion, and then thrashing those links against the surface until they break at the joint and fall off the body. It does so using a single autoregressive sequence model, a universal controller that learns in simulation when and how to simplify a robot's body through self-destruction and then adaptively controls the reduced morphology. The optimized policy successfully transfers to reality and generalizes to previously unseen kinematic trees, generating forward locomotion that is more effective than otherwise equivalent policies that randomly remove links or cannot remove any. This suggests that self-designing robots may be more successful than predesigned robots in some cases, and that kinematic self-destruction, though reductive and irreversible, could provide a general adaptive strategy for a wide range of robots.

Problem

• self-designing robots
• kinematic self-destruction
• morphological adaptation
• proprioceptive feedback
• autonomous robot design

Innovation

• kinematic self-destruction
• self-designing robots
• autoregressive sequence model
• proprioceptive feedback
• morphological adaptation