Towards Affordance-Aware Robotic Dexterous Grasping with Human-like
  Priors
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.
689c50c1fab6fdd2e52ac96d
Haoyu Zhao
689c50c1fab6fdd2e52ac96e
Linghao Zhuang
689c50c1fab6fdd2e52ac96f
Xingyue Zhao
689c50c1fab6fdd2e52ac970
Cheng Zeng
689c50c1fab6fdd2e52ac971
Haoran Xu
689c50c1fab6fdd2e52ac972
Yuming Jiang
689c50c1fab6fdd2e52ac973
Jun Cen
689c50c1fab6fdd2e52ac974
Kexiang Wang
689c50c1fab6fdd2e52ac975
Jiayan Guo
689c50c1fab6fdd2e52ac976
Siteng Huang
689c50c1fab6fdd2e52ac977
Xin Li
689c50c1fab6fdd2e52ac978
Deli Zhao
689c50c1fab6fdd2e52ac979
Hua Zou
Towards Affordance-Aware Robotic Dexterous Grasping with Human-like
  Priors
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.
AffordDex is a two-stage framework that learns a universal grasping policy with motion priors and object affordances, outperforming state-of-the-art methods in dexterous grasping and human-like postures.
trajectory imitator residual module Negative Affordance-aware Segmentation (NAA) privileged teacher-student distillation vision-based policy universal grasping human-like postures object affordances motion priors