Towards Affordance-Aware Robotic Dexterous Grasping with Human-like
  Priors
Authors: name: Haoyu Zhao, name: Linghao Zhuang, name: Xingyue Zhao, name: Cheng Zeng, name: Haoran Xu, name: Yuming Jiang, name: Jun Cen, name: Kexiang Wang, name: Jiayan Guo, name: Siteng Huang, name: Xin Li, name: Deli Zhao, name: Hua Zou
Organization: DAMO Academy

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.