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:与显形物体的相互作用对于移动机器人来说是一项艰巨但重要的任务。 为了应对这一挑战,我们提议建立一个新型的闭路控制管道,将操纵前科与基于取样的全体控制相结合。 我们引入了充分体现该代理器能力和性能的代理自觉支付能力的概念,并且我们表明它们的表现优于仅以终端效应几何为条件的最先进的对应方。 此外,还发现闭路支付能力推断让该代理器将任务分成多个非连续动作,并从失败和意外状态中恢复。 最后,该管道能够执行长视距移动操作任务,即在现实世界中以高成功率打开和关闭一个烤箱(打开:71%,关闭:72% )。