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CVPR2025顶会

Neural Motion Simulator Pushing the Limit of World Models in Reinforcement Learning

Chenjie Hao, Weyl Lu, Yifan Xu, Yubei Chen

2025年份
2顶会引用

摘要

Figure 1. This figure demonstrate the long-horizon precise prediction by the Neural Motion Simulators. In each of the three pictures, the first row shows the ground-truth states and the second row shows the predicted states with the same initial condition and actions sequence. Humanoid predicts for 30 steps with rendering every 3 steps; Panda predicts for 200 steps with rendering every 20 steps; myohand predicts for 400 steps with rendering every 40 steps.

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