# AdaDex: Learning Generalizable Dexterous Skills from a Single Trajectory via Hybrid Dexterous Mimicking

- Source: arXiv cs.RO
- Published: 2026-10-21T04:00:00.000Z
- RoboHot: https://robohot.news/en/items/bgeek29y25dpqgry5vxvqo4xn
- Original: https://arxiv.org/abs/2610.28628

## Summary

AdaDex is a goal-conditioned reinforcement learning framework that turns a few seconds of human hand-object trajectory (from motion capture data or HOI reconstruction from in-the-wild RGB(D) video) into generalizable dexterous skills. Its core, Hybrid Dexterous Mimicking, trains a shared policy across two sets of massively parallel environments: environments with dense motion tracking objectives inject the human prior, while environments with noised initial states and goals and a sparse object-success reward force the skill to work even when it deviates from the reference trajectory.