Zhuo Lin, Sirui Xu, Liuyu Bian, Yu-Xiong Wang, Liang-Yan Gui
InterEvolve is a test-time evolution framework for humanoid loco-manipulation tasks, enabling controllers to adapt to new tasks without retraining. It uses an object-aware forward-backward model and reward programs to induce new behaviors. This approach releases the controller's existing competence, leading to novel strategies.
InterEvolve's ability to adapt to new tasks without retraining has significant practical implications for real-world robotics applications, such as autonomous robots in dynamic environments. It can also reduce the need for extensive training data and improve the efficiency of robotic systems. This can lead to more flexible and autonomous robots.
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