Evo-World: A Lightweight Vision-Language-Action Model with Future Motion Prediction via Optical-Flow Supervision
Authors: Zile Zhong, Yuxin Du, Tao Lin, Haoxiao Wang, Wei Dai, Binhua Zuo, Bo Zhao
Organizations: School of AI, Shanghai Jiao Tong University · Zhejiang University · University of Electronic Science and Technology of China · QuicRobot
Vision-language-action (VLA) models provide a unified framework for visual understanding, language grounding, and robot control, yet anticipating how the scene will evolve beyond the current observation can provide additional information for control. Action supervision alone does not explicitly encourage such anticipation, while future-image prediction entangles task-relevant motion with largely static appearance. To address these limitations, we introduce Evo-World, a lightweight VLA model that augments policy learning with future motion prediction through optical-flow supervision, thereby providing a task-relevant predictive signal focused on motion rather than appearance. We further design the training procedure to preserve these predictive representations during policy learning, regulating representation diversity while decoupling representation learning from action optimization. Across simulation benchmarks, Evo-World demonstrates strong performance, achieving a tier-averaged success rate of 91.1% on Meta-World MT50 , with a 13.0% relative improvement over the non-predictive baseline. On real-world AgileX robots, Evo-World outperforms the evaluated baselines across single-arm and bimanual manipulation tasks while using only 0.87B parameters and delivering lower GPU memory usage and higher inference efficiency. These results demonstrate that future motion prediction through optical-flow supervision provides an effective and lightweight approach to enhancing the predictive capabilities of VLA models.
Figures & tables
Figure 1: Prediction targets for a VLA policy. (a) An action-only policy; (b) prediction of future appearance; and (c) prediction of future motion with optical-flow supervision. The two predictive branches condition action generation on latent representations. The flow illustration depicts the training target. The action-correlation labels express the motivation for motion supervision qualitatively.
Figure 2: Evo-World architecture. (a) The Motion Representation Module predicts a latent motion representation from DINOv2 image features and CLIP instruction features. In the dashed box, DINOv2 encodes an optical-flow RGB rendering as a target used only during training. (b) The InternVL3-1B vision-language backbone provides visual and linguistic context. (c) The current and predicted representations are concatenated and detached from action gradients, then projected into FiLM scale and shift parameters. These parameters modulate the backbone features to condition the flow-matching action expert for action generation.
Figure 3: Optical-flow supervision on Meta-World and RoboTwin. Panels (a) and (b) pair optical-flow renderings (top) with corresponding RGB observations (bottom) from Meta-World and RoboTwin, respectively. Colored regions emphasize motion, while nearly static regions appear close to white. Panel (c) shows the motion-to-color mapping: hue indicates displacement direction, and weaker motion produces colors closer to white.
Figure 4: Successful real-world executions of Evo-World on an AgileX robot. The rows show floral arrangement (top), a single-arm task, and mug hanging (middle) and microwave placement (bottom), two bimanual collaboration tasks.
Appendix figures & tables4 assets
Supplementary material from the paper’s appendix.
Appendix
Figure A1: Effect of SIGReg on latent representations. PCA projections for Evo-World (left pair) and the variant without SIGReg (right pair). Each pair shows zpred followed by zt . Colors indicate task difficulty, and black crosses mark the plotted means. For each representation, the corresponding panels use the same displayed axis scales. The annotations report the variance reduction factors when SIGReg is removed.
Figure A2: Example task sequences from Meta-World. From top to bottom: placing a basketball into a basket, opening a hinged door, opening a drawer, and opening a window. Each row shows six successive observations from left to right, with the corresponding language instruction below.
Figure A3: Example task sequences from RoboTwin. From top to bottom: pouring balls from a small container into a larger one, handing a microphone from one arm to the other, placing food items on a tray with both arms, and stacking a green block on a red block. Each row shows six successive observations from left to right, with the corresponding language instruction below.
Figure A4: Real-world experimental setup. Left: the teleoperation apparatus, with leader arms controlling the two follower arms and cameras providing an elevated view and left and right wrist views. Right: the Intel RealSense D435 camera and AgileX PiPER robot used in the setup. We collect 100 teleoperated demonstrations per task with varied object positions and use the collected data to separately fine-tune OpenVLA-OFT, π0 , and Evo-World.