# CosmoH2G：面向复杂空间运动物体操作的手到夹爪迁移数据集与基线方法

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-09-07 08:00
- AIHOT 分数：40
- AIHOT 链接：https://aihot.news/items/cmttmozwq0ejirofphanmf6ud
- 原文链接：https://arxiv.org/abs/2609.07498

## AI 摘要

CosmoH2G 提出一种可扩展的数据采集流程，构建了包含 6,189 个演示片段、覆盖 1,254 个独特物体的手-夹爪配对数据集，其空间复杂度显著高于现有基准。针对复杂空间运动中端到端生成夹爪位姿序列易累积误差的问题，该方法采用两阶段框架：先预测稀疏关键帧，再生成完整连续动作序列，并对平移进行后优化。仿真与真实机器人实验表明，该方法在复杂空间操作的手到夹爪迁移中显著优于传统基线。

## 正文

Transferring human hand demonstrations to robotic grippers has recently emerged as a cost-effective solution for robot learning. However, existing methods are largely confined to simple, planar tasks and fail to handle complex spatial movements (e.g., intricate trajectories involving rotations or flips) that are essential for robot manipulation. Motivated by this gap, we adopt an implicit, data-driven approach guided by fine-grained hand-pose motions. To this end, we introduce a scalable acquisition pipeline to collect hand-gripper paired demonstrations, governed by a rigorous protocol that prioritizes motion complexity and leverages a handheld gripper for seamless action mimicry. This yields a large-scale paired dataset comprising 6,189 episodes across 1,254 unique objects, exhibiting significantly higher spatial complexity than existing benchmarks. However, learning such complex mappings remains challenging. We observe that naive end-to-end generation of full gripper pose sequences is insufficient, as minor trajectory deviations compound rapidly under intricate dynamics. To address this, we propose a two-stage framework: Stage I predicts sparse gripper keyframes (initial and terminal) to simplify the mapping objective, while Stage II generates the full continuous action sequence conditioned on these keyframes. Furthermore, to mitigate cumulative drift, we keep the gripper's orientation being learned while post-optimizing its translation based on the grasping heuristic and kinematic consistency. In both simulation and real-robot experiments, our framework enables stable and precise hand-to-gripper transfer of complex spatial manipulations, significantly outperforming traditional baselines. Project page: https://cosmoh2g.github.io.