让机器人手部抓取更懂任务和场景，提升实际操作能力。
Task-Oriented Human Grasp Synthesis via Context- and Task-Aware Diffusers
- 用任务与场景感知的接触图指导抓取生成
- 在新数据集上抓取质量与任务完成率显著提升
- 适合需要真实场景理解的机器人抓取研究
本文研究任务导向的人类抓取合成，要求同时具备任务与场景感知。核心是任务感知的接触图：不同于传统仅关注物体与手部关系的接触图，我们的增强版图融合了场景和任务信息，对人机交互至关重要。提出两阶段流程：第一阶段基于场景和任务构建任务感知接触图；第二阶段利用该图生成任务导向的抓取姿态。引入新数据集与评估指标验证方法。实验表明，同时建模场景与任务能显著提升抓取质量和任务表现，优于现有方法。
原文摘要 · Abstract (English)
In this paper, we study task-oriented human grasp synthesis, a new grasp synthesis task that demands both task and context awareness. At the core of our method is the task-aware contact maps. Unlike traditional contact maps that only reason about the manipulated object and its relation with the hand, our enhanced maps take into account scene and task information. This comprehensive map is critical for hand-object interaction, enabling accurate grasping poses that align with the task. We propose a two-stage pipeline that first constructs a task-aware contact map informed by the scene and task. In the subsequent stage, we use this contact map to synthesize task-oriented human grasps. We introduce a new dataset and a metric for the proposed task to evaluate our approach. Our experiments validate the importance of modeling both scene and task, demonstrating significant improvements over existing methods in both grasp quality and task performance. See our project page for more details: https://hcis-lab.github.io/TOHGS/
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