Mimicking-Bench：通过人类模仿实现可泛化人形机器人-场景交互学习的基准
Mimicking-Bench: A Benchmark for Generalizable Humanoid-Scene Interaction Learning via Human Mimicking
AI总结：
本文提出首个通过模仿大规模人类动画参考实现可泛化人形机器人-场景交互学习的综合基准Mimicking-Bench，包含六项家庭任务、11K物体形状及23K技能参考，并构建完整技能学习流程评估了运动重定向、跟踪与模仿学习等方法。
摘要：
通过模仿人类数据来学习人形机器人与3D场景交互的通用技能，是一个对机器人学和现实应用具有重大意义的关键研究挑战。然而，现有方法和基准受限于小规模、人工收集的演示数据，缺乏有效探索场景几何泛化所需的通用数据集和基准支持。为了弥补这一空白，我们引入了Mimicking-Bench，这是首个为通过模仿大规模人类动画参考来实现可泛化人形机器人-场景交互学习而设计的综合基准。Mimicking-Bench包含六项家庭全身人形机器人-场景交互任务，涵盖11K种多样的物体形状，以及20K个合成和3K个真实世界的人类交互技能参考。我们构建了完整的人形机器人技能学习流程，并对运动重定向、运动跟踪、模仿学习及其各种组合的方法进行了基准测试。大量实验凸显了人类模仿对技能学习的价值，揭示了关键挑战和研究方向。
英文摘要：
Learning generic skills for humanoid robots interacting with 3D scenes by mimicking human data is a key research challenge with significant implications for robotics and real-world applications. However, existing methodologies and benchmarks are constrained by the use of small-scale, manually collected demonstrations, lacking the general dataset and benchmark support necessary to explore scene geometry generalization effectively. To address this gap, we introduce Mimicking-Bench, the first comprehensive benchmark designed for generalizable humanoid-scene interaction learning through mimicking large-scale human animation references. Mimicking-Bench includes six household full-body humanoid-scene interaction tasks, covering 11K diverse object shapes, along with 20K synthetic and 3K real-world human interaction skill references. We construct a complete humanoid skill learning pipeline and benchmark approaches for motion retargeting, motion tracking, imitation learning, and their various combinations. Extensive experiments highlight the value of human mimicking for skill learning, revealing key challenges and research directions.
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。
