ELAN4D: Embodiment-Centric 4D Supervision for Vision-Language-Action Models via Plug-and-Play Adaptation

2026-05-28

AI Summary

Existing vision-language-action (VLA) models predominantly rely on reactive policies and lack explicit modeling of future dynamics, limiting their generalization under out-of-distribution perturbations. This work proposes ELAN4D, a novel framework that leverages proprioceptive states to generate robot keypoint 4D trajectories—without requiring external trackers—as predictive spatiotemporal supervision signals. These trajectories are integrated into a pretrained VLA model via a lightweight, plug-and-play branch with gradient isolation, enabling lossless adaptation while preserving the original inference interface. Evaluated on LIBERO, LIBERO-Plus, RoboTwin2.0, and real-world tasks, ELAN4D significantly outperforms strong baselines and demonstrates markedly enhanced robustness and generalization under out-of-distribution perturbations such as changes in camera viewpoint, background, and scene layout.

Abstract

Vision-Language-Action (VLA) models have shown promise for robotic manipulation, yet most existing policies operate reactively by directly regressing actions from current observations, without explicitly modeling future dynamics. This limits their ability to generalize under out-of-distribution perturbations. To address this issue, we propose ELAN4D, an embodiment-centric, 4D-aware training framework that enhances VLA policies with future robot keypoint tracks as predictive spatio-temporal supervision. Using only forward kinematics from proprioceptive states, we derive 3D displacement tracks of robot keypoints, such as joints and the end-effector, with negligible preprocess cost. These tracks provide metric and compact supervision without requiring external trackers or reconstruction. A plug-and-play auxiliary branch with a lightweight track decoder injects this 4D signal into the action expert while preserving the pretrained vision-language backbone through gradient isolation. The track decoder is discarded during inference, leaving the base policy interface unchanged. Extensive experiments on LIBERO, LIBERO-Plus, RoboTwin2.0 and real-world manipulation tasks demonstrate that ELAN4D consistently improves over strong VLA baselines, achieving the best overall performance and substantial gains under out-of-distribution perturbations, including camera, background, and layout shifts. These results highlight the effectiveness of embodiment-centric 4D supervision for building more robust and generalizable manipulation policies.

Problem

• Vision-Language-Action models
• out-of-distribution generalization
• robotic manipulation
• future dynamics modeling
• embodiment

Innovation

• 4D supervision
• embodiment-centric
• plug-and-play adaptation
• VLA models
• future dynamics prediction