SmoothVLA: Aligning Vision-Language-Action Models with Physical Constraints via Intrinsic Smoothness Optimization

Researchers introduce SmoothVLA, a new reinforcement learning framework that improves robot control by optimizing both task performance and motion smoothness. The system addresses the trade-off between stability and exploration in Vision-Language-Action models, achieving 13.8% better smoothness than standard RL methods.