GenH2R：通过可扩展仿真、演示与模仿学习可泛化的人到机器人交接
GenH2R: Learning Generalizable Human-to-Robot Handover via Scalable Simulation, Demonstration, and Imitation
AI总结：
本文提出 GenH2R 框架，通过大规模仿真环境、百万级自动演示和带未来预测的 4D 模仿学习，使机器人能泛化接收未见物体与人机复杂轨迹交接，成功率较基线至少提升 10%。
摘要：
本文提出 GenH2R，这是一个用于学习可泛化的、基于视觉的人到机器人（H2R）交接技能的框架。其目标是使机器人具备可靠接收人类沿各种复杂轨迹递来的、具有未见几何形状物体的能力。我们通过一套综合方案大规模学习 H2R 交接来获得这种泛化能力，该方案包括程序化仿真资产生成、自动化演示生成和有效的模仿学习。我们利用大规模 3D 模型库、灵巧抓取生成方法以及基于曲线的 3D 动画，创建了一个名为 \simabbns 的 H2R 交接仿真环境，其场景数量比现有仿真器高出三个数量级。我们进一步提出一种便于蒸馏的演示生成方法，可自动生成一百万条适合学习的高质量演示。最后，我们提出一种由未来预测目标增强的 4D 模仿学习方法，将演示蒸馏为视觉-运动交接策略。在仿真器和真实世界中的实验评估表明，在所有情况下，其相较基线均有显著提升（成功率至少提高 10%）。项目页面为 https://GenH2R.github.io/。
英文摘要：
This paper presents GenH2R, a framework for learning generalizable vision-based human-to-robot (H2R) handover skills. The goal is to equip robots with the ability to reliably receive objects with unseen geometry handed over by humans in various complex trajectories. We acquire such generalizability by learning H2R handover at scale with a comprehensive solution including procedural simulation assets creation, automated demonstration generation, and effective imitation learning. We leverage large-scale 3D model repositories, dexterous grasp generation methods, and curve-based 3D animation to create an H2R handover simulation environment named \simabbns, surpassing the number of scenes in existing simulators by three orders of magnitude. We further introduce a distillation-friendly demonstration generation method that automatically generates a million high-quality demonstrations suitable for learning. Finally, we present a 4D imitation learning method augmented by a future forecasting objective to distill demonstrations into a visuo-motor handover policy. Experimental evaluations in both simulators and the real world demonstrate significant improvements (at least +10\% success rate) over baselines in all cases. The project page is https://GenH2R.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。
