AffordDex

AffordDex research team

AAAI 2026 research combining human motion priors with object-affordance constraints for dexterous grasping.

What you can explore

Explore where a hand should avoid contact and how the repository trains imitation, residual and vision-based policies using Shadow-hand task configurations.

Before you use it

Uses Isaac Gym and separately obtained datasets. The released task configuration is a research model, not proof of compatibility with a current commercial Shadow Hand. Results have not been reproduced by Dexhands.

Original contributors

Haoyu Zhao, Linghao Zhuang and collaborators; full author list in the original paper

License / access: No reuse license verified in the repository; check dataset and dependency terms separately

Official code & setup

Research paper

FROM REFERENCE TO FIRST EXPERIMENT

Start with the right setup.

INPUTObject geometry and human grasp priors

OUTPUTAffordance-aware grasp candidates

Environment: Isaac Gym
Embodiment: Shadow configuration

What you need

Follow the Python 3.10 environment and obtain the required UniDexGrasp and OakInk2 data.

First useful check

Inspect the data-processing, grasp-generation and evaluation stages separately.

Where it stops

Dataset permissions and code permissions are separate; no code reuse license was verified.

Original setup instructions ↗Work through the practical guide →

Documentation reviewed; not independently reproduced · reviewed 2026-09-24. Record the exact source revision when reproducing it.

Editorial reference · checked 2026-09-24

The world of dexterous hands.