TDSR-VLA: Transition-aware Denoising Sequence Representations for Vision-Language-Action

Vision–language–action (VLA) policies often become brit-tle under deployment distribution shift when only limited target-domaindemonstrations are available. Subgoal-image planning addresses this is-sue by providing an explicit visual target. However, the target imagespecifies what future scene to reach, not how the robot should transitiontoward it. We propose TDSR-VLA, a VLA framework that reuses condi-tioning states from a diffusion-based Vision Planner to guide action gen-eration. The Vision Planner predicts a subgoal image and provides tworepresentations: a Sequence Representation (SR) grounded in the currentobservation and an Updated Sequence Representation (USR) refined dur-ing denoising steps. A flow-matching Action Expert generates continuousaction chunks by injecting these representations through layer-wise pre-fix key–value conditioning. On real-world tasks, TDSR-VLA achieves anaverage OpenArm score of 0.83, outperforming GR00T N1.5 (0.74), π0(0.71), and SmolVLA (0.58), with the largest gains on object-cooperativetasks (0.88 vs. 0.70). On LIBERO, TDSR-VLA reaches 82.0% on Goaland 56.0% on Long without robot-demonstration pretraining, outper-forming OpenVLA on both suites and π0 on Long. Ablation studies showthat planner-side conditioning states provide action-relevant transitioncues, improving long-horizon manipulation under limited training data.