基于类人先验的可及性感知机器人灵巧抓取研究
Towards Affordance-Aware Robotic Dexterous Grasping with Human-like Priors
AI总结：
针对现有灵巧抓取方法忽视可及性定位与类人姿态的问题，提出两阶段训练的AffordDex框架，通过类人运动先验、残差适配、NAA模块及特权蒸馏实现通用抓取，实验表明其在多场景下显著优于基线。
摘要：
具备通用物体抓取能力的灵巧手是通用具身人工智能发展的基础。然而，现有方法局限于低层级抓取稳定性指标，忽略了对下游操作至关重要的可及性感知定位与类人姿态。为解决这些局限，我们提出AffordDex框架，通过两阶段训练学习兼具运动先验与物体可及性理解的通用抓取策略。第一阶段，轨迹模仿器在大规模人类手部运动语料上预训练，以植入自然运动的强先验。第二阶段，训练残差模块将通用类人运动适配到特定物体实例，该优化由两个组件关键引导：负可及性感知分割（NAA）模块识别功能不当接触区域，特权师生蒸馏过程确保最终视觉策略高度成功。广泛实验表明，AffordDex不仅实现通用灵巧抓取，姿态还保持显著类人性且接触位置功能适配，在已见物体、未见实例甚至全新类别上显著优于当前最优基线。
英文摘要：
A dexterous hand capable of generalizable grasping objects is fundamental for the development of general-purpose embodied AI. However, previous methods focus narrowly on low-level grasp stability metrics, neglecting affordance-aware positioning and human-like poses which are crucial for downstream manipulation. To address these limitations, we propose AffordDex, a novel framework with two-stage training that learns a universal grasping policy with an inherent understanding of both motion priors and object affordances. In the first stage, a trajectory imitator is pre-trained on a large corpus of human hand motions to instill a strong prior for natural movement. In the second stage, a residual module is trained to adapt these general human-like motions to specific object instances. This refinement is critically guided by two components: our Negative Affordance-aware Segmentation (NAA) module, which identifies functionally inappropriate contact regions, and a privileged teacher-student distillation process that ensures the final vision-based policy is highly successful. Extensive experiments demonstrate that AffordDex not only achieves universal dexterous grasping but also remains remarkably human-like in posture and functionally appropriate in contact location. As a result, AffordDex significantly outperforms state-of-the-art baselines across seen objects, unseen instances, and even entirely novel categories.
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。
