Title: BridgeVLA: Input-Output Alignment for Efficient 3D Manipulation Learning with Vision-Language Models
Venue: NeurIPS 2025
Accepted: yes
Keywords: 3D Robot Manipulation；Vision-Language Model；Vision-Language-Action Model；
Abstract: Recently, leveraging pre-trained vision-language models (VLMs) for building vision-language-action (VLA) models has emerged as a promising approach to effective robot manipulation learning. However, only few methods incorporate 3D signals into VLMs for action prediction, and they do not fully leverage the spatial structure inherent in 3D data, leading to low data efficiency. In this paper, we introduce a new paradigm for constructing 3D VLAs. Specifically, we first pre-train the VLM backbone to take 2D images as input and produce 2D heatmaps as output. Using this pre-trained VLM as the backbone, we then fine-tune the entire VLA model while maintaining alignment between inputs and outputs by: (1) projecting raw point cloud inputs into multi-view images, and (2) predicting heatmaps before generating the final action. Extensive experiments show that the resulting model, BridgeVLA, can learn 3D manipulation both efficiently and effectively. BridgeVLA outperforms state-of-the-art baselines across three simulation benchmarks. In RLBench, it improves the average success rate from 81.4\% to 88.2\%. In COLOSSEUM, it demonstrates significantly better performance in challenging generalization settings, boosting the average success rate from  56.7\% to 64.0\%. In GemBench, it surpasses all the comparing baseline methods in terms of average success rate. In real-robot experiments, BridgeVLA outperforms a state-of-the-art baseline method by 32\% on average. It generalizes robustly in multiple out-of-distribution settings, including visual disturbances and unseen instructions. Remarkably, it is able to achieve a success rate of 95.4\% on 10+ tasks with only 3 trajectories per task, while other VLA methods such as $\pi_{0}$ fail completely. Project Website: https://bridgevla.github.io/.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents BridgeVLA, a 3D vision-language-action (VLA) model that leverages the structural priors of 3D data (i.e., point clouds) while mitigating a large distributional shift from the original vision-language model training. A key idea of BridgeVLA is to transform 3D point cloud observation into multiple 2D images and further learn to predict a 2D heatmap highlighting regions of interest. 2D heatmaps are used to predict robotic actions. Experiments show that BridgeVLA outperforms several baselines on simulated environments (RLBench and COLOSSEUM) and real-world environments.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n\n- (S1) The manuscript proposes a new 3D VLA model for robotic tasks \n- (S2) The paper conducted extensive analyses across real-world and simulation evaluations\n- (S3) The paper is generally well-written\n\n**Weaknesses**\n\n- (W1) **Limited empirical justification for core claims**. The current manuscript mainly argues:\n  - (1) Current 3D VLA models [1,2] are sample inefficient since they do not fully exploit the spatial structure inherent in 3D data.\n  - (2) Using 3D data for fine-tuning models trained on 2D inputs introduces a large distributional shift that harms performance.\n- However, the authors did not validate these critical claims in their experiments. They only compared BridgeVLA to non-VLA baselines (e.g., RVT-2), without benchmarking against any existing 3D VLA models. This omission weakens the empirical support for their critique of prior work and leaves the practical advantages of the proposed method unsubstantiated.\n\n- (W2) **Lack of component-level analysis**. The paper claims superior performance over prior work across diverse environments, but it does not provide a deeper investigation into the sources of these improvements. Without detailed ablations or analysis isolating the contributions of individual design choices, it is difficult for the community to interpret where the gains come from or derive actionable insights for future work.\n\n- (W3) **Lack of clarity in core components**. Some technical details of the proposed method are insufficiently described. In Chapter 3, key components of BridgeVLA—such as convex upsampling and the loss functions—are not presented with enough precision. This lack of clarity hinders reproducibility and makes it difficult for readers to fully understand the technical underpinnings of the approach.\n\n**References**\n- [1] 3D-VLA: A 3D Vision-Language-Action Generative World Model. ICML 2024.\n- [2] SpatialVLA: Exploring Spatial Representations for Visual-Language-Action Model. RSS 2025.\n\nQUESTIONS:\nN/A\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-09"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nBridgeVLA is a 3D vision-language-action (VLA) model that addresses the misalignment between 3D inputs and 2D pre-trained vision-language models(VLMs). BridgeVLA first pre-trains a vision-language models to predict 2D object heatmaps on standard images, then fine-tunes it on 3D robot data by rendering RGB-D point clouds into three orthographic views and using the resulting heatmaps to drive end-effector translation, rotation, gripper, and collision predictions. This approach not only reconciles the 3D-2D modality gap but also outperforms prior 3D manipulation methods - achieving higher success rates on RLBench benchmarks.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1. Unified 2D Alignment : By learning a shared 2D heatmap representation in both pre-training and fine-tuning, BridgeVLA aligns 2D features with 3D robot data.\n2. Data Efficiency : BridgeVLA achieves strong task performance with very limited robot demonstrations (3 trajectories per task)\n3. Robustness & Generalization : BridgeVLA demonstrates resilience under visual disturbances and generalizes to novel objects and unseen object-skill combinations.\n\nWeakness:\n\n1. No Direct Baseline Comparison : The paper does not report head-to-head performance against other VLA methods (e.g., OpenVLA[2], π0[3], HAMSTER[1]).\n2. Limited Novelty : Prior works(HAMSTER[1]) already pre-train vision-language models on 2D images to learn spatial representations that a downstream 3D policy can directly leverage, and some even train the VLM to predict end-effector trajectories in the image plane. BridgeVLA's main technical departures are its specific 2D heatmap - based pre-training architecture, its orthographic projection and the addition of a motion planner after action prediction. To make the contribution clearer, the authors should demonstrate how their approach differs from prior works([1],[2],[3]) and pinpoint which components-such as heatmap prediction, orthographic projections, or the post-prediction motion planner-specifically drive the observed performance improvements.\n3. Unreported Inference Speed : Inference latency isn't measured. Real-time applicability remains underexplored.\n\n[1] Li, Yi, et al. \"Hamster: Hierarchical action models for open-world robot manipulation.\"arXiv preprint arXiv:2502.05485 (2025).\n\n[2] Kim, Moo Jin, et al. \"Openvla: An open-source vision-language-action model.\"arXiv preprint arXiv:2406.09246 (2024).\n\n[3] Black, Kevin, et al. \"π0: A vision-language-action flow model for general robot control, 2024.\"URL[https://arxiv](https://arxiv/). org/abs/2410.24164\n\nQUESTIONS:\nThe questions below extend the weakness outlined above:\n\n1. Prior work (e.g., HAMSTER[1]) already pre-trains on 2D images and then uses a 3D policy. How does BridgeVLA differ from HAMSTER?\n2. HAMSTER's([1]) 2D pre-training also includes a task for the VLM to predict object pixel locations in the image. Compared to that, what makes BridgeVLA's heatmap pre-training more spatially aware?\n3. In Table 1, can BridgeVLA be directly compared to other VLA methods on RLBench? Could you show a head-to-head comparison where OpenVLA[2], π₀[3], HAMSTER[1], etc., are fine-tuned using the same amount of robot data?\n4. Inference seems potentially slow - what is BridgeVLA's actual inference time (in HZ)?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-29"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposed, BridgeVLA a 3D vision-language-action (VLA) model that projects 3D inputs into multiple 2D images and uses 2D heatmaps for action prediction, aligning both input and output spaces for efficient robot manipulation learning. The authors train the model in 2 stages:\n\na) pre-training the VLM backbone to predict heatmaps with task relevant points, and\nb) training a policy on top of the heat maps to predict the end-effector pose of the robot.\n\nBridgeVLA achieves state-of-the-art results on the RL-Bench benchmark and shows significant robustness on the COLLESUM benchmark.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n\n- The method is sound and novel and furthers the boundary of 2D heatmap-based robotic manipulation.\n- Leveraging the RoboPoint dataset for pre-training is a very neat way of learning 2D heatmap priors for the downstream task of manipulation.\n- Well-written paper.\n\n\n**Weaknesses**:\n\n1. Comparison to alternative 3D VLA method like FP3 is missing. I'm curious if the heatmap-based approaches perform significantly better than natively trained 3D policies. (PointVLA doesn't open-source their code, so a comparison there isn't required).\n\n2. ACT [1] has become a strong BC baseline in recent years, so I believe that having the ACT baseline the way the Diffusion Policy baseline (3D diffuser actor) was added would make this paper stronger.\n\n----\n\n**References**\n\n[1] Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware, Tony Zhao et al., RSS 2023\n\nQUESTIONS:\n3. What is the inference time for the overall policy?\n\n4. In Table 4, what could be the reason that BridgeVLA performs on \"RO-TEXTURE\" than on \"NO Variations\"?\n\n5. There are no long-horizon tasks included in the evaluation (both in sim and real world). I'm wondering if the pipeline would still work when chained with multiple tasks. For instance, \"Put the Red Bull can on the bottom shelf and the Coke can on the top shelf.\" I hypothesize that the heatmap generation may fail (or maybe not) as it wasn't trained on such prompts. But any insights on this would be helpful.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nBridgeVLA is a 3D Vision-Language-Action (VLA) model that enhances robot manipulation by efficiently integrating 3D data with pre-trained vision-language models. It projects 3D inputs into 2D images for compatibility with VLMs and uses 2D heatmaps for action prediction, ensuring spatial consistency. A scalable pre-training phase further improves performance. Experiments show BridgeVLA outperforms state-of-the-art methods, achieving higher success rates with better sample efficiency.\n\nSTRENGTHS AND WEAKNESSES:\n1. We would recommend that the authors consider including ablation studies on the 2D heatmap prediction component.\n2.  Lack of Comparison with State-of-the-Art VLA Models: The current approach, which involves predicting 2D Heatmaps prior to action generation, is conceptually similar to methods like ECoT. Despite the authors' claim of using 3D point cloud data, the actual input to the VLA appears to be RGB data, warranting a direct comparison with ECoT. Furthermore, the authors should benchmark their method against contemporary VLA algorithms in a real experimental setting. Relevant comparisons include Pi\\_0, Co-Act RDT-1B,OpenVLA.OpenVLA-oft, 3D Diffusion Policy, and ACT.Additionally, we hope the authors can explore the model's performance on tasks such as folding clothes and folding pants.\n3.  Insufficient Experimental Validation of Cross-Modal Bridging:The paper asserts that the proposed method bridges the gap between 2D image-input Vision-Language Models (VLMs) and 3D inputs. However, most of the algorithms used for comparison do not utilize VLMs as their backbone, thereby failing to adequately demonstrate this claimed advantage.\n\nQUESTIONS:\nPlease refer to the weaknesses section above for details：\n 1.Absence of Ablation Studies\n 2. Lack of Comparison with State-of-the-Art VLA Models\n 3. Insufficient Experimental Validation of Cross-Modal Bridging\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 2, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-03"}