Physical Reasoning VLA Models for Enhancing Robotic Manipulation

Abstract

Vision-Language-Action (VLA) models have demonstrated strong performance in robot manipulation by leveraging pre-trained vision-language models to map observations directly to actions. However, existing approaches reason primarily at the visual or semantic level, lacking explicit understanding of the physical interactions that fundamentally govern manipulation tasks. In this paper, we propose Physics Reasoning VLA, a method that enables VLA models to explicitly reason about physical interactions prior to acting, grounded in two fundamental quantities: contact points, which specify where the target object interacts with the robot or surrounding environment, and contact forces, which describe the magnitude and direction of force applied at those locations. Rather than directly mapping observations to actions, our model first predicts contact points and forces at the pixel level via learnable physics queries, then incorporates the resulting physics-aware features alongside visual and language inputs to generate actions. To prevent physics reasoning from disrupting pre-trained visual and linguistic representations, we further introduce a hybrid attention mechanism that applies full attention over image, language, and proprioceptive tokens, while applying causal attention over physics query and action tokens. We evaluate our method on the RoboCasa simulation benchmark, demonstrating that physics reasoning consistently improves performance over the vanilla π0 baseline, with an average success rate improvement from 36.2% to 43.6%.