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. Recent advances in 3D visuomotor policies, such as the 3D Diffusion Policy (DP3), have shown promise in extending these capabilities to wilder environments. However, 3D visuomotor policies often rely on camera calibration and point-cloud segmentation, which present challenges for deployment on mobile robots like humanoids. In this work, we introduce the Improved 3D Diffusion Policy (iDP3), a novel 3D visuomotor policy that eliminates these constraints by leveraging egocentric 3D visual representations. We demonstrate that iDP3 enables a full-sized humanoid robot to autonomously perform skills in diverse real-world scenarios, using only data collected in the lab. Videos are available at: https://humanoid-manipulation.github.io

本研究解决了类人机器人在多样环境中自主操控能力受限的问题，提出了一种新的3D视觉运动策略，即改进的3D扩散策略（iDP3），其通过利用自我中心的3D视觉表示，克服了现有技术在移动机器人应用中的局限性。研究表明，iDP3使全尺寸类人机器人能够在多种真实场景中自主执行技能，显著提高了类人机器人的操作灵活性。

通用型类人机器人操控的改进3D扩散策略