仅用单一场景数据，让全尺寸人形机器人在真实环境中自主操作。
Generalizable Humanoid Manipulation with 3D Diffusion Policies
- 通过人类遥控采集类人动作数据，结合3D扩散策略学习。
- 在真实机器人上完成2000+次试验，实现跨场景泛化操作。
- 仅依赖机载计算，适合实际部署的机器人系统。
能够自主适应多样环境的人形机器人一直是机器人领域的目标。然而，由于难以获取可泛化的技能以及真实人形机器人数据成本高昂，目前人形机器人的自主操作仍局限于特定场景。本文构建了一个真实世界机器人系统来解决这一难题。该系统整合了：1）全身上肢遥控系统以采集类人机器人数据；2）具备可调高度小车与3D LiDAR传感器的25自由度人形机器人平台；3）改进的3D扩散策略算法，用于从含噪人类数据中学习。我们在真实机器人上运行超过2000次策略回放进行严格评估。结果表明，仅使用单一场景采集的数据并依靠机载计算，全尺寸人形机器人即可在多种真实场景中自主执行操作任务。视频演示见https://humanoid-manipulation.github.io。
原文摘要 · Abstract (English)
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 .
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