# Active Vision Might Be All You Need: Exploring Active Vision in Bimanual Robotic Manipulation

arXiv: https://arxiv.org/abs/2409.17435 · HTML: https://mlsanity.com/paper/2409.17435/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Placed in Robot manipulation imitation learning, one of 13 directions in Offline RL and imitation learning from data.
- First posted on arXiv 26 Sep 2024.

## Placed in

- Offline RL and imitation learning from data / [Robot manipulation imitation learning](https://mlsanity.com/offlinerl/robot-manipulation-imitation-learning/)

## Built on by (most cited first, 22 of 22)

- [Eye, Robot: Learning to Look to Act with a BC-RL Perception-Action Loop](https://mlsanity.com/paper/2506.10968/) (2025)
- [VITA: Vision-to-Action Flow Matching Policy](https://mlsanity.com/paper/2507.13231/) (2025)
- [AnyBimanual: Transferring Unimanual Policy for General Bimanual Manipulation](https://mlsanity.com/paper/2412.06779/) (2024)
- [ManiGaussian++: General Robotic Bimanual Manipulation with Hierarchical Gaussian World Model](https://mlsanity.com/paper/2506.19842/) (2025)
- [Look, Focus, Act: Efficient and Robust Robot Learning via Human Gaze and Foveated Vision Transformers](https://mlsanity.com/paper/2507.15833/) (2025)
- [BFA: Best-Feature-Aware Fusion for Multi-View Fine-grained Manipulation](https://mlsanity.com/paper/2502.11161/) (2025)
- [ActiveMimic: Egocentric Video Pretraining with Active Perception](https://mlsanity.com/paper/2606.06194/) (2026)
- [IN-RIL: Interleaved Reinforcement and Imitation Learning for Policy Fine-Tuning](https://mlsanity.com/paper/2505.10442/) (2025)
- [Towards Exploratory and Focused Manipulation with Bimanual Active Perception: A New Problem, Benchmark and Strategy](https://mlsanity.com/paper/2602.01939/) (2026)
- [TAVIS: A Benchmark for Egocentric Active Vision and Anticipatory Gaze in Imitation Learning](https://mlsanity.com/paper/2605.07943/) (2026)
- [Apple: Toward General Active Perception via Reinforcement Learning](https://mlsanity.com/paper/2505.06182/) (2025)
- [Observer-Actor: Active Vision Imitation Learning with Sparse-View Gaussian Splatting](https://mlsanity.com/paper/2511.18140/) (2025)
- [ProactiveBench: Benchmarking Proactiveness in Multimodal Large Language Models](https://mlsanity.com/paper/2603.19466/) (2026)
- [MarineFormer: A Spatio-Temporal Attention Model for USV Navigation in Dynamic Marine Environments](https://mlsanity.com/paper/2410.13973/) (2024)
- [Gaze on the Prize: Shaping Visual Attention with Return-Guided Contrastive Learning](https://mlsanity.com/paper/2510.08442/) (2025)
- [Look, Zoom, Understand: The Robotic Eyeball for Embodied Perception](https://mlsanity.com/paper/2511.15279/) (2025)
- [Real-World Reinforcement Learning of Active Perception Behaviors](https://mlsanity.com/paper/2512.01188/) (2025)
- [See Selectively, Act Adaptively: Dual-Level Structural Decomposition for Bimanual Robot Manipulation](https://mlsanity.com/paper/2606.13279/) (2026)
- [Bimanual Manipulation Within an 8 GB Budget: Zero-Copy Sensing and Quantized ACT on an Entry-Level Jetson](https://mlsanity.com/paper/2608.03938/) (2026)
- [ActiveScale: Scaling Active Perception for Robots across Model, Data, and Hardware](https://mlsanity.com/paper/2609.18514/) (2026)
- [EyeRobot 2.0: Active Gaze for Precise Manipulation without Wrist Cameras](https://mlsanity.com/paper/2610.03710/) (2026)
- [FoveaVLA: Foveation Actions Replace the Wrist Camera in VLAs](https://mlsanity.com/paper/2610.20064/) (2026)

## Abstract (arXiv)

Imitation learning has demonstrated significant potential in performing high-precision manipulation tasks using visual feedback. However, it is common practice in imitation learning for cameras to be fixed in place, resulting in issues like occlusion and limited field of view. Furthermore, cameras are often placed in broad, general locations, without an effective viewpoint specific to the robot's task. In this work, we investigate the utility of active vision (AV) for imitation learning and manipulation, in which, in addition to the manipulation policy, the robot learns an AV policy from human demonstrations to dynamically change the robot's camera viewpoint to obtain better information about its environment and the given task. We introduce AV-ALOHA, a new bimanual teleoperation robot system with AV, an extension of the ALOHA 2 robot system, incorporating an additional 7-DoF robot arm that only carries a stereo camera and is solely tasked with finding the best viewpoint. This camera streams stereo video to an operator wearing a virtual reality (VR) headset, allowing the operator to control the camera pose using head and body movements. The system provides an immersive teleoperation experience, with bimanual first-person control, enabling the operator to dynamically explore and search the scene and simultaneously interact with the environment. We conduct imitation learning experiments of our system both in real-world and in simulation, across a variety of tasks that emphasize viewpoint planning. Our results demonstrate the effectiveness of human-guided AV for imitation learning, showing significant improvements over fixed cameras in tasks with limited visibility. Project website: https://soltanilara.github.io/av-aloha/