Article

MAST-VLA: Multi-Skill Robot Manipulation with Skill-Conditioned Experts and Trajectory-Affordance Priors

MAST-VLA proposes a dual-conditioning framework that introduces skill-conditioned LoRA Mixture-of-Experts adapters into the LLM backbone and converts predicted affordance heatmaps into a patch-level trajectory-affordance spatial prior for the action head. In the CALVIN ABC→D long-horizon multi-skill cross-scene setting, it achieves an average completion length of 4.65 over five-subtask sequences, the best performance in that setting. Real-robot multi-skill sequence experiments further validate its deployability under real perception and control noise. The paper has been accepted to CoRL 2026.

Part of the event: MAST-VLA Multi-Skill Manipulation Framework Paper Released

This page provides a summary and source link only.