机器人通过自毁冗余部件自我重构，实现动态进化。
Robots that redesign themselves through kinematic self-destruction
- 用本体感觉反馈自主识别并破坏阻碍运动的连接部件。
- 在真实环境中实现比随机自毁或无法自毁的策略更优的前向移动。
- 适用于未见过的结构，适合研究自适应机器人与演化设计。
迄今为止所有机器人皆由外部预先设计后部署。本文展示了一种在生命周期中主动参与自身设计的机器人：从随机组装的躯体出发，仅依赖本体感觉反馈，通过动态‘雕刻’自身实现形态优化——识别阻碍运动的冗余连接，反复撞击地面直至关节断裂脱落。该过程由单一自回归序列模型控制，该模型在仿真中学习何时以及如何通过自毁简化结构，并适应性调控简化后的形态。优化策略成功迁移到真实环境，且泛化至此前未见的运动树结构，生成的前进运动效果优于等效的随机去除非关键部件或无法自毁的策略。这表明，在某些场景下，自设计机器人可能比预设计机器人更有效；而尽管自毁具有还原性和不可逆性，它仍可成为广泛机器人系统的一种通用适应策略。
原文摘要 · Abstract (English)
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.
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