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.

翻译：足部操作利用腿式机器人的足部进行移动操作，无需专用机械臂。虽然先前研究展示了盲操作和特定任务的足部操作技能，但未能考虑环境中的静态与动态障碍物。为解决这一局限性，我们提出一种基于强化学习的方法，训练全身障碍物感知策略，使其在跟踪足部位置指令的同时规避障碍物。尽管仅在仿真中的五种静态场景下训练策略，我们证明其能够泛化至具有不同数量和类型障碍物的未知环境。我们通过一系列仿真实验分析该方法性能，并成功将习得策略部署于ANYmal四足机器人，验证了其在遵循足部指令的同时规避静态与动态障碍物的能力。