# Tokens of Motion: Primitive Motion Learning for Human-Humanoid Skill Transfer

- Source: arXiv cs.RO
- Published: 2026-10-28T04:00:00.000Z
- RoboHot: https://robohot.news/en/items/ty8l9xzf1o0dnd68bf8093b6g
- Original: https://arxiv.org/abs/2610.37330

## Summary

The paper proposes the Adaptive Failure-Informed Learning (AFIL) framework, which uses a pretrained VLA to generate failure trajectories online as adaptive negative guidance for diffusion- and flow-based VLA policies, avoiding the need for handcrafted failure modes or human intervention for recovery. The method jointly trains two action generators (DAGs) for success and failure on a shared vision-language backbone, and at sampling time adjusts the guidance strength according to the distance between the success and failure distributions at each diffusion step. On in-domain and out-of-domain, short-horizon and long-horizon robotic manipulation tasks, AFIL improves task success rate and robustness compared with existing VLA baselines.