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Reinforcement learning in linear embedding space unlocks generalizable control across soft robot configurations

This work introduces a generalizable control system that enables rapid adaptation across 33 soft robot configurations via reinforcement learning in a shared Koopman embedding space, enabling real-world skills in carpentry and bartending style tasks.

本摘要由 Max Robotics 编辑，原文版权归 Nature.com 所有。