DexMimicGen：基于模仿学习的双臂灵巧操作自动化数据生成
DexMimicGen: Automated Data Generation for Bimanual Dexterous Manipulation via Imitation Learning
AI总结：
针对双臂灵巧机器人数据采集困难的问题，提出大规模自动化数据生成系统DexMimicGen，仅凭60个人类演示生成2.1万条轨迹，并验证了real-to-sim-to-real流程在真实分拣任务中的有效性。
摘要：
从人类演示中进行模仿学习是教授机器人操作技能的有效手段。但由于涉及大量的成本和人力投入，数据采集成为更广泛应用这一范式的主要瓶颈。目前，学界对类人等双臂灵巧机器人的模仿学习有着浓厚兴趣。然而，由于同时控制多个机械臂和多指手的挑战，这里的数据采集甚至更加困难。仿真中的自动化数据生成是满足这一数据需求的一种极具吸引力且可扩展的替代方案。为此，我们提出了DexMimicGen，这是一个大规模自动化数据生成系统，能够根据少量人类演示，为配备灵巧手的类人机器人合成运动轨迹。我们提供了一系列双臂灵巧操作场景下的仿真环境，涵盖多种操作行为以及双臂间不同的协同要求。我们仅凭60个人类源演示，在这些任务中生成了2.1万（21K）个演示数据，并研究了几项数据生成和策略学习决策对智能体性能的影响。最后，我们提出了一个real-to-sim-to-real（真-仿-真）流程，并将其部署在真实世界的类人机器人罐头分拣任务中。生成的数据集、仿真环境及更多结果请见 https://dexmimicgen.github.io/
英文摘要：
Imitation learning from human demonstrations is an effective means to teach robots manipulation skills. But data acquisition is a major bottleneck in applying this paradigm more broadly, due to the amount of cost and human effort involved. There has been significant interest in imitation learning for bimanual dexterous robots, like humanoids. Unfortunately, data collection is even more challenging here due to the challenges of simultaneously controlling multiple arms and multi-fingered hands. Automated data generation in simulation is a compelling, scalable alternative to fuel this need for data. To this end, we introduce DexMimicGen, a large-scale automated data generation system that synthesizes trajectories from a handful of human demonstrations for humanoid robots with dexterous hands. We present a collection of simulation environments in the setting of bimanual dexterous manipulation, spanning a range of manipulation behaviors and different requirements for coordination among the two arms. We generate 21K demos across these tasks from just 60 source human demos and study the effect of several data generation and policy learning decisions on agent performance. Finally, we present a real-to-sim-to-real pipeline and deploy it on a real-world humanoid can sorting task. Generated datasets, simulation environments and additional results are at https://dexmimicgen.github.io/
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。
