本项目基于 **NVIDIA Omniverse / Isaac Lab** 大规模并行张量仿真框架，针对目前工业界最前沿的足式平台（**Unitree G1/H1 人形机器人, Unitree Go1 / ANYmal-C 机器狗**）进行了强化学习 (PPO) 步态策略的从零训练。项目深度介入了**域随机化 (Domain Randomization)** 与 **面向硬件的奖励塑形 (Hardware-Aware Reward Shaping)**，成功实现了机器人无需视觉输入、仅靠本体感觉（Proprioception）即可在崎岖地形（Rough Terrain）与极限冰面下保持动态平衡的鲁棒控制。
☆19Mar 4, 2026Updated 7 months ago
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