基于可负担性的四足机器人层级强化学习操控
Affordance-Based Hierarchical Reinforcement Learning for Quadruped Pedipulation
AI总结：
提出三级层级强化学习框架，利用姿态和交互点可负担性引导导航与操控策略，在仿真和真实环境中实现自主物体操控。
摘要：
四足机器人的物体操控能力是一个开放的研究挑战。虽然先前的研究侧重于低级策略学习，任务执行仍依赖于专家设计的高级轨迹。自主选择目标物体上的可负担交互点和可负担机器人基座姿态消除了对预设计轨迹的需求。本研究提出了一个三级层级强化学习（RL）框架，利用姿态可负担性来引导导航策略，而导航策略驱动运动策略。此外，操控策略由交互点可负担性引导，实现四足机器人的物体中心姿态对齐和有效的末端执行器操控规划。我们在IsaacSim生态系统中训练所提出的框架，并在仿真和真实环境中进行评估。我们在仿真中研究了姿态可负担性在多个场景下的有效性，同时在真实环境中验证了各种物体交互任务，形成了物体交互数据集。结果表明，所提出的框架能够基于可负担性自主识别候选姿态，并在无需人类引导的情况下成功执行真实世界中的物体操控任务。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
