Sign up to save your podcasts
Or
Share 【第181期】ASAP：两阶段框架弥合仿真与现实物理之间的差距
Share to email
Share to Facebook
Share to X
Share to email
Share to Facebook
Share to X
Or
【第181期】ASAP：两阶段框架弥合仿真与现实物理之间的差距
Seventy3: 用NotebookLM将论文生成播客，让大家跟着AI一起进步。
今天的主题是：ASAP: Aligning Simulation and Real-World Physics for Learning Agile Humanoid Whole-Body SkillsSummary
The provided research paper introduces ASAP, a novel two-stage framework designed to bridge the gap between simulated and real-world physics for humanoid robots, enabling them to perform complex, agile movements. The first stage involves pre-training control policies in simulation using human motion data. The second stage deploys these policies in the real world to collect data and train a "delta action model" that learns to compensate for discrepancies in dynamics. This model is then integrated back into the simulator to fine-tune the control policies, allowing for more accurate and agile real-world execution. Experiments demonstrate that ASAP significantly improves the ability of humanoid robots to perform challenging tasks, outperforming existing methods in both simulated and real environments. The work highlights a promising direction for transferring skills learned in simulation to physical robots, ultimately leading to more versatile and capable humanoids.
该研究提出了 ASAP，一种创新的两阶段框架，旨在弥合仿真与现实物理之间的差距，使人形机器人能够执行复杂且灵活的运动。第一阶段在仿真环境中使用人类运动数据进行控制策略的预训练。第二阶段将这些策略部署到现实环境，采集数据并训练一个**“增量动作模型”（delta action model），用于补偿动力学差异。随后，该模型被集成回仿真环境，以微调控制策略**，从而实现更精准、灵活的现实世界执行。实验结果表明，ASAP 显著提升了人形机器人完成高难度任务的能力，无论在仿真还是现实环境中均优于现有方法。本研究为仿真训练迁移至现实机器人提供了一条有效途径，推动人形机器人向更多功能、更智能的方向发展。
原文链接：https://arxiv.org/abs/2502.01143
View all episodes
By 任雨山Seventy3: 用NotebookLM将论文生成播客，让大家跟着AI一起进步。
今天的主题是：ASAP: Aligning Simulation and Real-World Physics for Learning Agile Humanoid Whole-Body SkillsSummary
The provided research paper introduces ASAP, a novel two-stage framework designed to bridge the gap between simulated and real-world physics for humanoid robots, enabling them to perform complex, agile movements. The first stage involves pre-training control policies in simulation using human motion data. The second stage deploys these policies in the real world to collect data and train a "delta action model" that learns to compensate for discrepancies in dynamics. This model is then integrated back into the simulator to fine-tune the control policies, allowing for more accurate and agile real-world execution. Experiments demonstrate that ASAP significantly improves the ability of humanoid robots to perform challenging tasks, outperforming existing methods in both simulated and real environments. The work highlights a promising direction for transferring skills learned in simulation to physical robots, ultimately leading to more versatile and capable humanoids.
该研究提出了 ASAP，一种创新的两阶段框架，旨在弥合仿真与现实物理之间的差距，使人形机器人能够执行复杂且灵活的运动。第一阶段在仿真环境中使用人类运动数据进行控制策略的预训练。第二阶段将这些策略部署到现实环境，采集数据并训练一个**“增量动作模型”（delta action model），用于补偿动力学差异。随后，该模型被集成回仿真环境，以微调控制策略**，从而实现更精准、灵活的现实世界执行。实验结果表明，ASAP 显著提升了人形机器人完成高难度任务的能力，无论在仿真还是现实环境中均优于现有方法。本研究为仿真训练迁移至现实机器人提供了一条有效途径，推动人形机器人向更多功能、更智能的方向发展。
原文链接：https://arxiv.org/abs/2502.01143