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VLA-4D: Embedding 4D Awareness into Vision-Language-Action Models for SpatioTemporally Coherent Robotic Manipulation

Hanyu Zhou, Chuanhao Ma, Gim Hee Lee

arXiv:2511.17199v1cs.CV

TL;DR

VLA models still face challenges in spatiotemporally coherent manipulation, including coarse visual reasoning and limited temporal control. VLA-4D adds space and time to visual and action representations; the authors report state-of-the-art performance across robotic manipulation tasks.

  • Problem

    VLA models struggle with spatiotemporally coherent manipulation: single-image inputs can yield coarse visual reasoning, coordinate mismatches impair action precision, and 3D VLAs may lack temporally coherent control.

    Source: §1 · p. 1

  • Method

    VLA-4D fuses 3D positions and 1D time into visual features and extends spatial action parameters with temporal variables for spatiotemporal action prediction.

    Source: §1 · p. 2, §3.1 · p. 4, §3.2 · p. 5

  • Results

    VLA-4D achieves state-of-the-art performance across various robotic manipulation tasks.

    Source: §1 · p. 2

  • Takeaways & Limitations

    In the analyzed setting, 4D visual features correspond to continuous spatiotemporal manifolds and action features to continuous spatiotemporal clusters, alongside highest task success and reduced completion time.

    Source: §5.4 · p. 8

    The authors state that VLA-4D still faces challenges, although the supplied passage does not specify them.

    Source: §5.4 · p. 9

Abstract

from arXiv · show

Vision-language-action (VLA) models show potential for general robotic tasks, but remain challenging in spatiotemporally coherent manipulation, which requires fine-grained representations. Typically, existing methods embed 3D positions into visual representations to enhance the spatial precision of actions. However, these methods struggle to achieve temporally coherent control over action execution. In this work, we propose VLA-4D, a general VLA model with 4D awareness for spatiotemporally coherent robotic manipulation. Our model is guided by two key designs: 1) 4D-aware visual representation. We extract visual features, embed 1D time into 3D positions for 4D embeddings, and fuse them into a unified visual representation via a cross-attention mechanism. 2) Spatiotemporal action representation. We extend conventional spatial action representations with temporal information to enable the spatiotemporal planning, and align the multimodal representations into the LLM for spatiotemporal action prediction. Within this unified framework, the designed visual and action representations jointly make robotic manipulation spatially-smooth and temporally-coherent. In addition, we extend the VLA dataset with temporal action annotations for fine-tuning our model. Extensive experiments have been conducted to verify the superiority of our method across different tasks of robotic manipulation.

1. Introduction

VLA models struggle with fine-grained manipulation that is both spatially precise and temporally coherent. VLA-4D addresses this by enriching visual and action representations with spatiotemporal information and extending training data with temporal annotations.

1. Introduction

  • Existing VLA models struggle with fine-grained spatiotemporally coherent manipulation, with single-image inputs and image-to-robot coordinate mismatches identified as key issues.The paper also notes that 3D positional cues improve spatial precision but do not directly ensure temporally coherent action plans.Source: §1 · pp. 1–2
  • VLA-4D combines a 4D-aware visual representation with a spatiotemporal action representation to support fine-grained visual reasoning and action planning.It encodes 3D positions and 1D time into visual features and adds varying temporal variables to conventional spatial action parameters.Source: §1 · p. 2
  • Cross-attention fuses 3D positions and 1D time into visual features, while multimodal alignment enables the LLM to produce spatiotemporal actions.The model processes video-derived visual and geometric features and extends spatial control parameters with temporal variables.Source: §1 · p. 2
  • The authors extend the LIBERO dataset with temporal action annotations for fine-tuning and report state-of-the-art performance across various robotic manipulation tasks.The paper presents these as contributions of the proposed framework.Source: §1 · p. 2

2. Related Work

Prior VLA work uses visual and linguistic inputs for action planning, while 3D and 4D variants add spatial or temporal visual cues. The cited 4D approaches strengthen visual reasoning, but the paper distinguishes its focus on explicitly representing spatiotemporal action controls.

2. Related Work

  • VLA models commonly co-embed discrete action tokens with visual and linguistic inputs in an LLM for scene-grounded robotic motion planning.The paper frames the challenge as a mismatch between coarse image-and-text knowledge and the precise, continuous parameters robotic operations demand.Source: §2 · p. 2
  • 3D VLA models [12] [13] [14] [29] embed positional information in visual features to strengthen spatial reasoning and optimize spatial action controls, but remain limited in temporal reasoning and control.The paper names sequential state chaos and potential operational idling as examples.Source: §2 · p. 2
  • VLA-4D’s two stages fuse 4D spatiotemporal embeddings into visual features, then extend action parameters into the spatiotemporal domain and align them with multimodal representations.The architecture caption identifies cross-attention as the visual-feature fusion mechanism and an LLM for robotic action prediction.Source: Figure 2 · p. 3
  • Prior 4D VLA methods add spatial and temporal information to visual representations, but the paper says they do not explicitly improve temporal action-planning precision or overall execution coherence.VLA-4D instead combines 4D-aware visual representations with spatiotemporal action parameters.Source: §2 · p. 3
  • Niu et al. [15] learn 4D point trajectories from video for visual representations and transfer their distribution to robotic control; Zhang et al. [16] fuse frame indices with 3D positional embeddings.The paper describes Zhang et al.’s approach as mitigating temporal-state ambiguity during execution.Source: §2 · p. 3

3. Our VLA-4D

VLA-4D combines 4D-aware visual reasoning with spatiotemporal action planning. Its visual and action representations are designed to support spatially fine-grained, temporally coherent robotic manipulation.

3. Our VLA-4D

  • The framework has two stages: cross-attention fusion of 4D visual embeddings, followed by spatiotemporal action representation aligned with multimodal inputs.Together, these stages support finer-grained visual reasoning and action planning.Source: §3 · pp. 3–4

3.1. 4D-Aware Visual Representation

  • 3D positions and 1D time are embedded together, then fused with visual features to represent scene geometry, spatial localization, dynamic patterns, and temporal action states.The visual design uses cross-attention to combine the spatiotemporal embeddings with visual features.Source: §3.1 · p. 4, Figure 3 · p. 4

3.2. SpatioTemporal Action Representation

  • The action representation adds step-level duration to spatial controls, which comprise end-effector translation, rotation, and gripper state.The paper describes spatial controls as supporting fine-grained operations and temporal parameters as promoting continuity and coherence; the time variable is predefined from robot and task configurations.Source: §3.2 · pp. 4–5, Figure 4 · p. 5
  • Visual, linguistic, and proprioceptive tokens are aligned for an LLM and action head to predict spatiotemporal actions, trained with an L1 loss.At inference, predictions use historical videos, proprioceptive states, and task-specific textual instructions.Source: §3.2 · p. 5

4. Dataset and Training Pipeline

The section extends LIBERO with video-based inputs and temporal action annotations, then trains VLA-4D through 4D vision-language alignment followed by robotic-task fine-tuning. The pipeline initializes multimodal understanding from pretrained models.

4.1. Robotic Dataset

  • LIBERO is extended with timestamped multi-view videos, depths, proprioceptive states, and temporal action annotations, yielding 40 subtasks and 150k paired vision–language–action samples.The extensions support fine-tuning while retaining the original spatial action parameters; the dataset includes four benchmark settings.Source: §4.1 · p. 5

4.2. Training Pipeline

  • Stage 1 trains 4D visual representations on 3D/4D vision-language datasets with LLM loss, updating cross-attention, embeddings, projector, and LoRA-tuned LLM while freezing the vision and geometry encoders and action head.The pretrained models initialize multimodal understanding before fine-tuning.Source: §4.2 · pp. 5–6
  • Stage 2 fine-tunes on modified LIBERO with spatiotemporal action annotations and action loss, updating the action head and projector while LoRA-tuning cross-attention and LLM components.The remaining modules are frozen during this stage.Source: §4.2 · p. 6

5. Experiments

Across LIBERO comparisons, VLA-4D reports higher success and shorter completion times, including on zero-shot tasks; its 4D representation also yields more coherent trajectories. Ablations and discussion examine the contributions of spatial and temporal cues, fusion, encoding, and training strategy.

5.1. Experiment Setup

  • LIBERO evaluation uses task success rate and completion time for both fine-tuning across robotic subtasks and zero-shot evaluation on a selected subset.Experiments in Sections 5.3 and 5.4 use LIBERO-Spatial and LIBERO-Goal.Source: §5.1 · p. 7

5.2. Comparison with State-of-the-Art Models

  • VLA-4D achieves higher task success and the shortest completion time among competing methods on fine-tuned tasks, with higher success rates and shorter times across multiple zero-shot tasks.The trajectory comparison reports smooth global paths and stable local speeds for VLA-4D, unlike the redundant motion of the 2D model and local speed fluctuations of the 3D model.Source: §5.2 · p. 7, Figure 6 · p. 7

5.3. Ablation Study

  • Spatial and temporal visual embeddings substantially improve performance, while feature fusion further raises the performance ceiling; adding temporal action parameters reduces completion time after spatial actions establish a relatively high success rate.Video input provides a preliminary baseline, 4D cues substantially improve success and completion time, and proprioceptive states add a minor but consistent gain.Source: §5.3 · pp. 7–8, Table 2 · p. 7, Table 3 · p. 7

5.4. Discussion

  • Compared with 3D visual features, 4D features form a continuous spatiotemporal manifold with continuous action features and are associated with higher success and shorter completion time.The spatiotemporal encoding model performs better than the model without it; the authors say dedicated encoding supports stable convergence and prevents feature-space collapse.Source: §5.4 · p. 8, Table 5 · p. 8
  • Attention-based fusion outperforms concatenation and weighting by dynamically adjusting visual-feature weights according to 4D embeddings.The authors also report that multi-stage training combining stages 1 and 2 improves performance over stage 2 alone; deployment in unseen real-world environments remains challenging due to factors such as mechanical wear and calibration drift.Source: §5.4 · pp. 8–9, Table 6 · p. 8, Table 7 · p. 8

6. Conclusion

VLA-4D combines 4D-aware visual features with spatiotemporal action representations to support precise, coherent robotic actions. The work also extends a VLA dataset with temporal action annotations and reports benchmark experiments and ablations verifying the method’s superiority.

6. Conclusion

  • VLA-4D fuses 3D positions and 1D time into visual features and incorporates temporal variables into spatial action parameters for spatiotemporal reasoning and planning.The model uses multimodal alignment to produce precise and coherent robot actions.Source: §6 · p. 9
  • The authors extend a representative VLA dataset with temporal action annotations to fine-tune the model for spatiotemporal robotic manipulation.Source: §6 · p. 9
  • Experiments and ablations on benchmark datasets verify the reported superiority of VLA-4D.Source: §6 · p. 9

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