通过轨迹生成学习可泛化的工具使用技能
Learning Generalizable Tool-use Skills through Trajectory Generation
AI总结：
针对智能体使用新工具操控可变形物体的问题，提出以工具点云序列生成并对齐工具使用轨迹的单一模型，仅用单工具演示即可跨工具泛化，并在仿真和真实世界中验证其优异性能。
摘要：
能够高效使用工具的自主系统可以协助人类完成烹饪和清洁等许多常见任务。然而，当前系统在适应新工具方面尚不足以达到人类水平的智能。以往基于可供性的工作通常对环境作出强假设，无法扩展到更复杂、接触丰富的任务。在本工作中，我们应对这一挑战，探索智能体如何学习使用此前未见过的工具来操控可变形物体。我们提出学习一个工具使用轨迹的生成模型，将轨迹表示为一系列工具点云，该模型可泛化到不同的工具形状。给定任意新工具，我们首先生成一条工具使用轨迹，然后优化工具位姿序列，使其与生成的轨迹对齐。我们在四个不同且具有挑战性的可变形物体操控任务上训练单个模型，每个任务仅使用来自一种工具的演示数据。该模型可泛化到各种新工具，并显著优于基线方法。我们进一步在真实世界中使用未见过的工具测试训练得到的策略，其取得了与人类相当的性能。更多材料可在我们的项目网站获取：https://sites.google.com/view/toolgen。
英文摘要：
Autonomous systems that efficiently utilize tools can assist humans in completing many common tasks such as cooking and cleaning. However, current systems fall short of matching human-level of intelligence in terms of adapting to novel tools. Prior works based on affordance often make strong assumptions about the environments and cannot scale to more complex, contact-rich tasks. In this work, we tackle this challenge and explore how agents can learn to use previously unseen tools to manipulate deformable objects. We propose to learn a generative model of the tool-use trajectories as a sequence of tool point clouds, which generalizes to different tool shapes. Given any novel tool, we first generate a tool-use trajectory and then optimize the sequence of tool poses to align with the generated trajectory. We train a single model on four different challenging deformable object manipulation tasks, using demonstration data from only one tool per task. The model generalizes to various novel tools, significantly outperforming baselines. We further test our trained policy in the real world with unseen tools, where it achieves the performance comparable to human. Additional materials can be found on our project website: https://sites.google.com/view/toolgen.
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。
