arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2209.05802cs.RO
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学习Agent感知的与铰接对象的闭环交互示能

Learning Agent-Aware Affordances for Closed-Loop Interaction with Articulated Objects

ETH Zurich(苏黎世联邦理工学院);The University of Queensland(昆士兰大学)

Giulio Schiavi, Paula Wulkop, Giuseppe Rizzi, Lionel Ott, Roland Siegwart, Jen Jen Chung

摘要:

与关节式物体的交互是移动机器人的一项具有挑战性但重要的任务。为了应对这一挑战,我们提出了一种新的闭环控制流水线,它集成了操纵先验的启示估计与基于采样的全身控制。我们引入的概念,代理意识的启示,充分反映了代理的能力和实施例,我们表明,他们优于他们的国家的最先进的同行,这是唯一的条件上的末端效应几何形状。此外,闭环示能表示推理被发现允许代理将任务划分为多个非连续运动,并从失败和意外状态中恢复。最后,管道能够在现实世界中执行长时间移动操作任务,即打开和关闭烤箱,成功率很高(打开:71%,关闭:72%)。

英文摘要:

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%).

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。

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