让仿人机器人通过视觉自主规划步态并保持平衡，无需人工标注数据。
iWalker: Imperative Visual Planning for Walking Humanoid Robot
- 用两层指令学习优化步态与全身平衡，实现端到端视觉导航。
- 在仿真与真实环境均成功行走，能从任意无标签数据中自学习。
- 适合研究自主机器人、强化学习与具身智能的开发者参考。
仿人机器人作为服务于人类环境的基础平台，在多种任务中具有重要意义。尽管仿人机器人已研究数十年，多数系统仍依赖复杂的模块化框架，导致灵活性差且感知、规划、执行各模块间误差累积。为此，我们提出一种端到端的感知-规划-执行行走系统，实现基于视觉的障碍规避与足部步态规划，同时维持全身平衡。设计了两种基于指令学习（IL）的双层优化机制，分别用于模型预测步态规划与全身平衡控制，实现仿人机器人行走的自监督学习。该方法使机器人可从任意无标签数据中学习，显著提升适应性与泛化能力。我们将该方法命名为iWalker，并在仿真与真实环境中验证其有效性，代表了迈向自主仿人机器人的重要进展。
原文摘要 · Abstract (English)
Humanoid robots, designed to operate in human-centric environments, serve as a fundamental platform for a broad range of tasks. Although humanoid robots have been extensively studied for decades, a majority of existing humanoid robots still heavily rely on complex modular frameworks, leading to inflexibility and potential compounded errors from independent sensing, planning, and acting components. In response, we propose an end-to-end humanoid sense-plan-act walking system, enabling vision-based obstacle avoidance and footstep planning for whole body balancing simultaneously. We designed two imperative learning (IL)-based bilevel optimizations for model-predictive step planning and whole body balancing, respectively, to achieve self-supervised learning for humanoid robot walking. This enables the robot to learn from arbitrary unlabeled data, improving its adaptability and generalization capabilities. We refer to our method as iWalker and demonstrate its effectiveness in both simulated and real-world environments, representing a significant advancement toward autonomous humanoid robots.
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