arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2408.08282cs.ROcs.AIcs.LG
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基于落地语言模型的人形机器人运动-操作自主行为规划

Autonomous Behavior Planning For Humanoid Loco-manipulation Through Grounded Language Model

Istituto Italiano di Tecnologia(意大利技术研究院);Universita di Genova(热那亚大学)

Jin Wang, Arturo Laurenzi, Nikos Tsagarakis

AI总结:

针对人形机器人在非结构化环境中自主运动-操作的难题,提出基于大语言模型的行为规划框架,可自主规划执行并纠正故障,经仿真与真实环境实验验证了有效性。

摘要:

使人形机器人能够在非结构化环境中自主执行运动-操作任务,是实现具身智能的关键且极具挑战性的目标。这要求机器人能够在长周期任务中规划动作与行为,同时利用多模态感知任务执行与高层规划之间的偏差。近年来,大语言模型(LLMs)已在机器人控制任务中展现出强大的规划与推理能力,可理解和处理语义信息,还具备对多模态输入进行分析判断与决策的可用性。为将LLMs的能力应用于人形机器人运动-操作任务,我们提出了一种新型基于语言模型的框架,该框架可让机器人在给定文本指令下自主规划行为与底层执行,同时监测并纠正任务执行过程中可能出现的故障。为系统评估该框架在LLMs落地方面的表现,我们构建了用于任务规划的机器人“动作”与“感知”行为库,并使用CENTAURO机器人在仿真与真实环境中开展了移动操作任务与实验,验证了该方法在具备自主行为规划能力的机器人任务中的有效性与应用价值。

英文摘要:

Enabling humanoid robots to perform autonomously loco-manipulation in unstructured environments is crucial and highly challenging for achieving embodied intelligence. This involves robots being able to plan their actions and behaviors in long-horizon tasks while using multi-modality to perceive deviations between task execution and high-level planning. Recently, large language models (LLMs) have demonstrated powerful planning and reasoning capabilities for comprehension and processing of semantic information through robot control tasks, as well as the usability of analytical judgment and decision-making for multi-modal inputs. To leverage the power of LLMs towards humanoid loco-manipulation, we propose a novel language-model based framework that enables robots to autonomously plan behaviors and low-level execution under given textual instructions, while observing and correcting failures that may occur during task execution. To systematically evaluate this framework in grounding LLMs, we created the robot 'action' and 'sensing' behavior library for task planning, and conducted mobile manipulation tasks and experiments in both simulated and real environments using the CENTAURO robot, and verified the effectiveness and application of this approach in robotic tasks with autonomous behavioral planning.

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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