**ActiveVLA: Injecting Active Perception into Vision-Language-Action Models for Precise 3D Robotic Manipulation** **Zhenyang Liu**1,2, **Yongchong Gu**1, **Yikai Wang**3, **Xiangyang Xue**1,†, **Yanwei Fu**1,2,† 1Fudan University, 2Shanghai Innovation Institute, 3Nanyang Technological University †Corresponding Authors [![Paper](https://img.shields.io/badge/Paper-Arxiv-b31b1b.svg)](https://arxiv.org/abs/2601.08325v1) [![Project Page](https://img.shields.io/badge/Project-Website-blue.svg)](https://zhenyangliu.github.io/ActiveVLA/) [![Video](https://img.shields.io/badge/Video-YouTube-red.svg)](https://zhenyangliu.github.io/ActiveVLA)
--- ## 📢 News & Roadmap This repository is the official implementation of **ActiveVLA**. We are currently preparing the code and data for release. Please stay tuned! - [ ] **Release the Code** (Training & Inference scripts). - [ ] **Release Pre-trained Models**. - [ ] **Release Evaluation Scripts** (RLBench, COLOSSEUM, GemBench). - [ ] **Release Real-Robot Control Code**. --- ## 📖 Abstract Most existing Vision-Language-Action (VLA) models rely on static, wrist-mounted cameras that provide a fixed, end-effector-centric viewpoint. This setup limits perceptual flexibility: the agent cannot adaptively adjust its viewpoint or camera resolution according to the task context, leading to failures in long-horizon tasks or fine-grained manipulation due to occlusion and lack of detail. We propose **ActiveVLA**, a novel vision-language-action framework that explicitly integrates **active perception** into robotic manipulation. Unlike passive perception methods, ActiveVLA empowers robots to: 1. **Actively Select Viewpoints:** Autonomously determine optimal camera perspectives to maximize visibility and task relevance while minimizing occlusions. 2. **Active 3D Zoom-in:** Selectively enhance high-resolution views of task-critical regions within the 3D scene. By dynamically refining its perceptual input, ActiveVLA achieves superior adaptability and performance in complex scenarios. Experiments show that ActiveVLA outperforms state-of-the-art baselines on **RLBench**, **COLOSSEUM**, and **GemBench**, and transfers seamlessly to real-world robots. --- ## 🚀 Method: ActiveVLA We propose a coarse-to-fine active perception framework that integrates 3D spatial reasoning with vision-language understanding. The pipeline consists of two main stages: 1. **Critical Region Localization (Coarse Stage):** Projects 3D inputs onto multi-view 2D projections to identify critical 3D regions via heatmaps. 2. **Active Perception Optimization (Fine Stage):** * **Active Viewpoint Selection:** Uses a hypothesis testing strategy to choose optimal viewpoints that maximize amodal relevance and diversity. * **Active 3D Zoom-in:** Applies a virtual optical zoom effect to improve resolution in key areas for precise manipulation. > **Note:** For more visualizations and real-world robot demos, please visit our [**Project Page**](https://zhenyangliu.github.io/ActiveVLA). --- ## 📊 Results ActiveVLA achieves state-of-the-art performance across multiple benchmarks: - **RLBench:** Achieves an average success rate of **91.8%**, ranking 1st in 10 tasks. - **COLOSSEUM:** Demonstrates superior robustness with a **65.9%** success rate in challenging generalization scenarios. - **GemBench:** Outperforms all baselines with strong adaptability across diverse tasks. - **Real World:** High success rates in occlusion-heavy tasks (e.g., retrieving items from drawers, handling occluded objects). --- ## 📝 Citation If you find our work useful in your research, please consider citing: ```bibtex @misc{liu2026activevlainjectingactiveperception, title={ActiveVLA: Injecting Active Perception into Vision-Language-Action Models for Precise 3D Robotic Manipulation}, author={Zhenyang Liu and Yongchong Gu and Yikai Wang and Xiangyang Xue and Yanwei Fu}, year={2026}, eprint={2601.08325}, archivePrefix={arXiv}, primaryClass={cs.RO}, url={https://arxiv.org/abs/2601.08325}, }