为拟合物体操纵的物理常识知识而引入分析概念
Physically Ground Commonsense Knowledge for Articulated Object Manipulation with Analytic Concepts
AI总结：
本文提出通过引入分析概念，将语义级常识知识接地到物理世界，以提升机器人对关节物体的通用精确操纵能力。
摘要：
我们人类依赖广泛常识知识来与大量和各类物体互动。同样，这种常识知识对机器人成功开发通用物体操纵技能至关重要。尽管多模态大语言模型（MLLMs）的最新进展展示了其在获取常识知识和进行常识推理方面的出色能力，但将这些由MLLMs生成的语义级知识有效地接地到物理世界，以彻底指导机器人进行通用的关节物体操纵仍是一个未被充分解决的挑战。为此，我们引入了分析概念，这些概念是基于数学符号程序性定义的，可以直接由机器计算和模拟。通过利用分析概念作为桥接MLLMs推断出的语义级知识与机器人实际操作的物理世界之间的桥梁，我们可以利用具有物理信息表示的物体结构和功能知识，然后使用这种物理接地的知识来指导机器人控制策略，以实现通用和精确的关节物体操纵。在现实世界和模拟中的广泛实验证明了我们方法的优越性。
英文摘要：
We humans rely on a wide range of commonsense knowledge to interact with an extensive number and categories of objects in the physical world. Likewise, such commonsense knowledge is also crucial for robots to successfully develop generalized object manipulation skills. While recent advancements in Multi-modal Large Language Models (MLLMs) have showcased their impressive capabilities in acquiring commonsense knowledge and conducting commonsense reasoning, effectively grounding this semantic-level knowledge produced by MLLMs to the physical world to thoroughly guide robots in generalized articulated object manipulation remains a challenge that has not been sufficiently addressed. To this end, we introduce analytic concepts, procedurally defined upon mathematical symbolism that can be directly computed and simulated by machines. By leveraging the analytic concepts as a bridge between the semantic-level knowledge inferred by MLLMs and the physical world where real robots operate, we can figure out the knowledge of object structure and functionality with physics-informed representations, and then use the physically grounded knowledge to instruct robot control policies for generalized and accurate articulated object manipulation. Extensive experiments in both real world and simulation demonstrate the superiority of our approach.
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。
