让四足机器人像手脚并用般避障抓取，无需额外机械臂。
Perceptive Pedipulation with Local Obstacle Avoidance
- 基于强化学习训练全身避障策略，实时跟踪脚位指令。
- 仅在5种静态场景训练，即可泛化到未知障碍环境。
- 已在ANYmal机器人上验证，支持静动态障碍规避。
腿式机器人通过腿部执行移动操作（pedipulation），无需专用机械臂。以往研究虽展示了无视觉反馈和任务特定的操控能力，但未能考虑环境中的静态与动态障碍物。为此，我们提出一种基于强化学习的全身感知避障策略，可同时跟踪脚部位置指令并避开障碍物。尽管仅在五种不同静态场景中进行仿真训练，该策略仍能泛化至包含不同数量与类型障碍物的未知环境。通过一系列仿真实验分析性能，并成功部署于ANYmal四足机器人，验证其在绕行静态与动态障碍物的同时精准追踪脚部指令的能力。实验视频见 sites.google.com/leggedrobotics.com/perceptive-pedipulation。
原文摘要 · Abstract (English)
Pedipulation leverages the feet of legged robots for mobile manipulation, eliminating the need for dedicated robotic arms. While previous works have showcased blind and task-specific pedipulation skills, they fail to account for static and dynamic obstacles in the environment. To address this limitation, we introduce a reinforcement learning-based approach to train a whole-body obstacle-aware policy that tracks foot position commands while simultaneously avoiding obstacles. Despite training the policy in only five different static scenarios in simulation, we show that it generalizes to unknown environments with different numbers and types of obstacles. We analyze the performance of our method through a set of simulation experiments and successfully deploy the learned policy on the ANYmal quadruped, demonstrating its capability to follow foot commands while navigating around static and dynamic obstacles. Videos of the experiments are available at sites.google.com/leggedrobotics.com/perceptive-pedipulation.
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