Explicit Feasibility Supervision Boosts VLA Robot Learning

A new study from arXiv investigates whether adding explicit physical feasibility supervision improves Vision-Language-Action (VLA) models for robotics. VLA models map multimodal inputs to robot actions via imitation learning but typically lack direct supervision for constraints like obstacle avoidance or kinematic feasibility. Researchers propose a geometry-grounded feasibility objective integrated into a diffusion-based VLA policy. Using obstacle-aware manipulation as a controlled test, empirical results show that augmenting training with this explicit feasibility signal enhances policy performance. The study provides systematic evidence that structured geometric guidance can benefit VLA learning without additional data or complex engineering.