通用人形机器人操作与3D扩散策略
Generalizable Humanoid Manipulation with 3D Diffusion Policies
AI总结：
本研究构建了集成遥操作、25自由度人形平台与改进3D扩散策略的系统，仅用单场景数据即可实现多样环境下的自主操作。
摘要：
能够在多样化环境中自主操作的人形机器人一直是机器人学家的长期目标。然而，人形机器人的自主操作在很大程度上局限于单一特定场景，这主要是由于获取通用技能的困难以及野外人形机器人数据的高昂成本。在这项工作中，我们构建了一个真实世界的机器人系统来解决这一挑战性问题。我们的系统主要集成了：1）一个全身上半身机器人遥操作系统，用于获取类人机器人数据；2）一个具有高度可调推车和3D LiDAR传感器的25自由度人形机器人平台；以及3）一种改进的3D扩散策略学习算法，使人形机器人能够从嘈杂的人类数据中学习。我们在真实机器人上运行了超过2000个回合的策略部署，以进行严格的策略评估。借助该系统，我们展示了仅使用在单一场景中收集的数据且仅依赖机载计算，一个全尺寸人形机器人便能在多样化的真实世界场景中自主执行技能。视频可在https://humanoid-manipulation.github.io获取。
英文摘要：
Humanoid robots capable of autonomous operation in diverse environments have long been a goal for roboticists. However, autonomous manipulation by humanoid robots has largely been restricted to one specific scene, primarily due to the difficulty of acquiring generalizable skills and the expensiveness of in-the-wild humanoid robot data. In this work, we build a real-world robotic system to address this challenging problem. Our system is mainly an integration of 1) a whole-upper-body robotic teleoperation system to acquire human-like robot data, 2) a 25-DoF humanoid robot platform with a height-adjustable cart and a 3D LiDAR sensor, and 3) an improved 3D Diffusion Policy learning algorithm for humanoid robots to learn from noisy human data. We run more than 2000 episodes of policy rollouts on the real robot for rigorous policy evaluation. Empowered by this system, we show that using only data collected in one single scene and with only onboard computing, a full-sized humanoid robot can autonomously perform skills in diverse real-world scenarios. Videos are available at https://humanoid-manipulation.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。
