用动态异构图学习机器人双臂揉面，让机械臂像人一样操作软物体。
Learning Goal-oriented Bimanual Dough Rolling Using Dynamic Heterogeneous Graph Based on Human Demonstration
- 用动态异构图统一表示状态与策略，捕捉物体形变和动作关系。
- 在仿真和真实人形机器人上实现接近人类的揉面行为。
- 支持从人类示范中学习，适合软体物体操控场景的研究者。
软体物体操作对机器人构成重大挑战，需要有效的状态表征和操作策略学习技术。状态表征需捕捉环境的动态变化，而操作策略学习则需建立机器人动作与状态演变之间的关联以达成特定目标。为此，本文提出一种基于动态异构图的新方法，用于学习面向目标的软体物体操作策略。该模型利用图结构作为状态与策略学习的统一表示，通过动态图提取物体动力学与操作策略的关键信息，并支持示范数据的整合，实现引导式策略学习。为验证方法有效性，设计了揉面任务，在可微分仿真器和真实人形机器人上进行实验。此外，还进行了多项消融实验，结果表明该方法在实现类人行为方面表现更优。
原文摘要 · Abstract (English)
Soft object manipulation poses significant challenges for robots, requiring effective techniques for state representation and manipulation policy learning. State representation involves capturing the dynamic changes in the environment, while manipulation policy learning focuses on establishing the relationship between robot actions and state transformations to achieve specific goals. To address these challenges, this research paper introduces a novel approach: a dynamic heterogeneous graph-based model for learning goal-oriented soft object manipulation policies. The proposed model utilizes graphs as a unified representation for both states and policy learning. By leveraging the dynamic graph, we can extract crucial information regarding object dynamics and manipulation policies. Furthermore, the model facilitates the integration of demonstrations, enabling guided policy learning. To evaluate the efficacy of our approach, we designed a dough rolling task and conducted experiments using both a differentiable simulator and a real-world humanoid robot. Additionally, several ablation studies were performed to analyze the effect of our method, demonstrating its superiority in achieving human-like behavior.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。