RFUniverse：面向具身智能的多物理场仿真平台
RFUniverse: A Multiphysics Simulation Platform for Embodied AI
AI总结：
提出RFUniverse多物理场仿真平台，支持刚体、多体动力学及气固、流固、热传递耦合效应，用于具身智能体家务任务训练，并通过强化学习与规划控制基准验证了其有效性。
摘要：
多物理场现象，即涉及不同物理定律方面的耦合效应，在现实世界中无处不在，并且在执行日常家务任务时经常遇到。寻求协助或替代人类劳动力的智能体需要学会在家务任务场景中应对此类现象。为了使智能体具备此类能力，研究社区需要一个仿真环境，该环境能够作为这些智能体训练过程的测试平台，并具备支持多物理场耦合效应的能力。尽管许多成熟的多物理场仿真软件已在工业生产中得到采用，但这些技术尚未应用于机器人学习或具身智能研究。为弥合这一差距，我们提出了一种名为RFUniverse的新型仿真环境。该仿真器不仅能计算刚体和多体动力学，还能计算日常生活中常见的多物理场耦合效应，如气固相互作用、流固相互作用和热传递。由于该仿真器独特的多物理场能力，我们可以在部署到现实世界之前，在仿真环境中对涉及多物理场耦合效应引起的复杂动力学的任务进行基准测试。RFUniverse提供多种接口，使用户能够以多种方式与虚拟世界交互，这对于学习、规划和控制是有帮助且必不可少的。我们使用强化学习对三项任务进行了基准测试，包括食物切割、推水和接毛巾。我们还使用经典的规划-控制范式评估了推黄油任务。该仿真器在多物理场耦合效应的计算方面增强了物理仿真能力。
英文摘要：
Multiphysics phenomena, the coupling effects involving different aspects of physics laws, are pervasive in the real world and can often be encountered when performing everyday household tasks. Intelligent agents which seek to assist or replace human laborers will need to learn to cope with such phenomena in household task settings. To equip the agents with such kind of abilities, the research community needs a simulation environment, which will have the capability to serve as the testbed for the training process of these intelligent agents, to have the ability to support multiphysics coupling effects. Though many mature simulation software for multiphysics simulation have been adopted in industrial production, such techniques have not been applied to robot learning or embodied AI research. To bridge the gap, we propose a novel simulation environment named RFUniverse. This simulator can not only compute rigid and multi-body dynamics, but also multiphysics coupling effects commonly observed in daily life, such as air-solid interaction, fluid-solid interaction, and heat transfer. Because of the unique multiphysics capacities of this simulator, we can benchmark tasks that involve complex dynamics due to multiphysics coupling effects in a simulation environment before deploying to the real world. RFUniverse provides multiple interfaces to let the users interact with the virtual world in various ways, which is helpful and essential for learning, planning, and control. We benchmark three tasks with reinforcement learning, including food cutting, water pushing, and towel catching. We also evaluate butter pushing with a classic planning-control paradigm. This simulator offers an enhancement of physics simulation in terms of the computation of multiphysics coupling effects.
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。
