HumanoidMimicGen: 通过全身规划生成行走操作数据
HumanoidMimicGen: Data Generation for Loco-Manipulation via Whole-Body Planning
AI总结：
提出HumanoidMimicGen方法，通过全身规划自动生成人形机器人行走操作演示数据，在模拟基准上使联合训练的策略性能提升20%。
摘要：
模仿学习是训练人形机器人行走和操作的一种有前景的方法，但它需要大量演示，而这些演示通过遥操作收集耗时且困难。现有的数据生成算法可以自动合成操作器的演示，但它们在类人机器人上效果不佳，因为其高维复合动作空间涉及手臂、腿和躯干。我们提出HumanoidMimicGen，一种生成人形机器人腿部行走操作数据的方法。我们的方法将少量源演示中的接触丰富的全身技能适应到新状态，并泛化到物体姿态的变化。通过将这些单臂和双臂技能与全身运动规划和操作规划交替进行，该方法在多样化的场景和布局中生成稳定、无碰撞的数据。为了评估我们的方法，我们引入了一个新的模拟行走操作基准，包含九个测试人形机器人行走操作能力的多样化任务。在那里，我们证明HumanoidMimicGen自动生成用于模仿学习的大规模数据集，并能够系统研究数据生成和策略学习决策如何影响模型性能。我们表明，与仅使用真实世界数据训练的策略相比，与HumanoidMimicGen生成的数据联合训练的全身视觉运动策略性能提升20%。
英文摘要：
Imitation learning is a promising approach for training humanoid robots to both walk and manipulate, but it requires a large number of demonstrations, which are time-intensive and difficult to collect via teleoperation. Existing data-generation algorithms can automatically synthesize demonstrations for manipulators, but they are ineffective on humanoids because their high-dimensional composite action spaces involve arms, legs, and torsos. We present HumanoidMimicGen, a method for generating humanoid legged loco-manipulation data. Our method adapts contact-rich whole-body skills from a handful of source demonstrations to new states, generalizing across changes in object pose. By interleaving these single- and dual-arm skills with whole-body locomotion and manipulation planning, the method generates stable, collision-free data across diverse scenes and layouts. To evaluate our approach, we introduce a new simulated loco-manipulation benchmark containing nine diverse tasks that test humanoid loco-manipulation capabilities. There, we demonstrate that HumanoidMimicGen automatically generates large datasets for imitation learning and enables a systematic study of how data generation and policy learning decisions impact model performance. We show that whole-body visuomotor policies co-trained with data generated by HumanoidMimicGen outperform those trained only on real-world data by 20%.
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。
