# H2R-Bench：评估视频世界模型的人到机器人操作视频生成基准

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-08-13 08:00
- AIHOT 分数：44
- AIHOT 链接：https://aihot.virxact.com/items/cmsshdnzq033kroffyrggylfp
- 原文链接：https://arxiv.org/abs/2608.13049

## AI 摘要

H2R-Bench 提出用于评估跨具身人到机器人操作视频生成的基准，要求模型将第一视角人类演示转换为指定具身下的机器人操作视频。基准涵盖目标状态完成、动作事件完成、功能接触迁移、具身正确性和视频质量五个维度，并评测了六个操作族、两种机器人具身上的十一个最新视频生成模型。结果显示当前视频世界模型在具身一致性、功能交互和任务执行上仍存在明显不足。

## 正文

Large-scale manipulation data is essential for robot learning, yet collecting robot demonstrations remains expensive and difficult to scale. Meanwhile, abundant egocentric human manipulation videos provide rich behavioral experiences, but transferring them across embodiments remains challenging due to differences between human hands and robotic end-effectors. Recent advances in video world models offer a promising pathway to synthesize robot-centric manipulation videos from human observations, while their cross-embodiment transfer capability remains largely unexplored. Therefore, we introduce H2R-Bench, a benchmark for evaluating cross-embodiment human-to-robot manipulation video generation, where models transform egocentric human demonstrations into robot manipulation videos under specified embodiments. Each benchmark instance contains a human demonstration video, target embodiment constraints, and source-grounded annotations covering task goals, action events, functional contacts, and object responses. H2R-Bench evaluates generated videos through five dimensions, including goal-state completion, action-event completion, functional contact transfer, embodiment correctness, and general video quality. We benchmark eleven state-of-the-art video generation models across six manipulation families and two robot embodiments. Our evaluation reveals that current video world models remain limited in human-to-robot manipulation transfer: even leading models often fail in embodiment consistency, functional interaction, and task execution. H2R-Bench provides a systematic diagnostic framework for evaluating whether video world models can bridge the human-to-robot embodiment gap and convert human manipulation observations into robot-centric training resources.