Interactions with articulated objects are a challenging but important task for mobile robots. To tackle this challenge, we propose a novel closed-loop control pipeline, which integrates manipulation priors from affordance estimation with sampling-based whole-body control. We introduce the concept of agent-aware affordances which fully reflect the agent's capabilities and embodiment and we show that they outperform their state-of-the-art counterparts which are only conditioned on the end-effector geometry. Additionally, closed-loop affordance inference is found to allow the agent to divide a task into multiple non-continuous motions and recover from failure and unexpected states. Finally, the pipeline is able to perform long-horizon mobile manipulation tasks, i.e. opening and closing an oven, in the real world with high success rates (opening: 71%, closing: 72%).

翻译：铰接物体的交互是移动机器人面临的一项具有挑战性但至关重要的任务。为应对这一挑战，我们提出了一种新颖的闭环控制流水线，该流水线将可供性估计的操纵先验与基于采样的全身控制相结合。我们引入了“感知智能体的可供性”概念，该概念充分反映了智能体的能力与具身特性，并证明其在性能上优于仅依赖于末端执行器几何形状的现有最先进方法。此外，闭环可供性推理使智能体能够将任务分解为多个非连续运动，并从故障和意外状态中恢复。最后，该流水线能够在现实世界中以高成功率执行长时域移动操纵任务（例如打开烤箱：71%，关闭烤箱：72%）。