四足机器人自主选抓点与姿态，实现无需人工设计轨迹的物体操作。
Affordance-Based Hierarchical Reinforcement Learning for Quadruped Pedipulation
- 分三层强化学习框架，用姿态与交互点可操作性引导决策。
- 在仿真和真实场景中成功完成多种物体操作任务，无需人工轨迹设计。
- 适合研究机器人自主操作、具身智能与层次化强化学习的学者。
四足机器人执行物体操作仍是开放性挑战。以往研究多关注低层策略学习，但任务执行仍依赖专家设计的高层轨迹。本研究提出一种三级分层强化学习框架，利用姿态可操作性引导导航策略，导航策略驱动运动策略；同时，通过交互点可操作性指导抓取策略，实现机器人以物体为中心的姿态对齐与末端执行器有效操作规划。在IsaacSim环境中训练并评估该框架，在仿真中验证了姿态可操作性在多场景下的有效性，在真实世界中完成了多种物体交互任务，并构建了物体交互数据集。结果表明，该框架可自主识别具有可操作性的候选姿态，并在无人工干预下成功完成真实世界的物体操作任务。
原文摘要 · Abstract (English)
The object manipulation capabilities of quadruped robots is an open research challenge. While previous studies have focused on low-level policy learning, task execution still relies on expert-designed high-level trajectories. Autonomous selection of both an affordable interaction point on the target object and an affordable robot base pose removes the need for pre-designed trajectories. This study proposes a three-level hierarchical reinforcement learning (RL) framework that utilizes pose affordances to guide the navigation policy, while the navigation policy drives the locomotion policy. In addition, the pedipulation policy is guided by interaction-point affordances, enabling object-centric pose alignment of the quadruped robot and effective end-effector manipulation planning. We train the proposed framework in the IsaacSim ecosystem and evaluate it in both simulation and real-world settings. We investigate the effectiveness of pose affordance across multiple scenarios in simulation while various object interaction tasks are validated on real-world setting forming an object-interaction dataset. The results show that the proposed framework can autonomously identify candidate poses based on their affordance and successfully execute object manipulation tasks in the real world without human guidance.
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