Action Intent as a Bridge: Transferring Manipulation Knowledge from Human-Centric Videos to Robots
Overview
Vision-Language-Action (VLA) models face limitations due to reliance on expensive real-robot data. This research introduces Action-Intent Transfer (AIT), a two-stage method that extracts clean action representations from human-centric videos, disentangles implicit noise, and aligns human and robot domains within a shared intent space. Extensive experiments in simulation and real-world environments show that AIT effectively transfers human manipulation knowledge and consistently outperforms prior baselines.
Generated from attributed reports · Updated 1 hours ago
Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
No current evidence-backed claims. Missing information remains not reported.
Report timeline
Follow attributed reports and material updates.
- arXiv Robotics — research abstractsSignalAction Intent as a Bridge: Transferring Manipulation Knowledge from Human-Centric Videos to Robots
Vision-Language-Action (VLA) models face limitations due to reliance on expensive real-robot data. This research introduces Action-Intent Transfer (AIT), a two-stage method that extracts clean action representations from human-centric videos, disentangles implicit noise, and aligns human and robot domains within a shared intent space. Extensive experiments in simulation and real-world environments show that AIT effectively transfers human manipulation knowledge and consistently outperforms prior baselines.
Event coverage history
There is not enough continuous observation data to show a trend.
Timezone · UTC
Article dates follow your selected timezone. Briefing editions use Hong Kong time (UTC+8).