# Towards Exploratory and Focused Manipulation with Bimanual Active Perception: A New Problem, Benchmark and Strategy

arXiv: https://arxiv.org/abs/2602.01939 · HTML: https://mlsanity.com/paper/2602.01939/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 29 references that are in the map.
- First posted on arXiv 2 Feb 2026.

## Builds on (most cited first, 21 of 21)

- [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://mlsanity.com/paper/2303.04137/) (2023)
- [π0: A Vision-Language-Action Flow Model for General Robot Control](https://mlsanity.com/paper/2410.24164/) (2024)
- [Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware](https://mlsanity.com/paper/2304.13705/) (2023)
- [AgiBot World Colosseo: A Large-scale Manipulation Platform for Scalable and Intelligent Embodied Systems](https://mlsanity.com/paper/2503.06669/) (2025)
- [Unleashing Large-Scale Video Generative Pre-training for Visual Robot Manipulation](https://mlsanity.com/paper/2312.13139/) (2023)
- [Open-TeleVision: Teleoperation with Immersive Active Visual Feedback](https://mlsanity.com/paper/2407.01512/) (2024)
- [Reactive Diffusion Policy: Slow-Fast Visual-Tactile Policy Learning for Contact-Rich Manipulation](https://mlsanity.com/paper/2503.02881/) (2025)
- [Galaxea Open-World Dataset and G0 Dual-System VLA Model](https://mlsanity.com/paper/2509.00576/) (2025)
- [Bunny-VisionPro: Real-Time Bimanual Dexterous Teleoperation for Imitation Learning](https://mlsanity.com/paper/2407.03162/) (2024)
- [3D-ViTac: Learning Fine-Grained Manipulation with Visuo-Tactile Sensing](https://mlsanity.com/paper/2410.24091/) (2024)
- [RoboEXP: Action-Conditioned Scene Graph via Interactive Exploration for Robotic Manipulation](https://mlsanity.com/paper/2402.15487/) (2024)
- [GR-MG: Leveraging Partially Annotated Data via Multi-Modal Goal-Conditioned Policy](https://mlsanity.com/paper/2408.14368/) (2024)
- [ViTacFormer: Learning Cross-Modal Representation for Visuo-Tactile Dexterous Manipulation](https://mlsanity.com/paper/2506.15953/) (2025)
- [Vision in Action: Learning Active Perception from Human Demonstrations](https://mlsanity.com/paper/2506.15666/) (2025)
- [ASID: Active Exploration for System Identification in Robotic Manipulation](https://mlsanity.com/paper/2404.12308/) (2024)
- [Eye, Robot: Learning to Look to Act with a BC-RL Perception-Action Loop](https://mlsanity.com/paper/2506.10968/) (2025)
- [Active Vision Might Be All You Need: Exploring Active Vision in Bimanual Robotic Manipulation](https://mlsanity.com/paper/2409.17435/) (2024)
- [Empowering Embodied Manipulation: A Bimanual-Mobile Robot Manipulation Dataset for Household Tasks](https://mlsanity.com/paper/2405.18860/) (2024)
- [Active Exploration for Robotic Manipulation](https://mlsanity.com/paper/2210.12806/) (2022)
- [Haptic-ACT: Bridging Human Intuition with Compliant Robotic Manipulation via Immersive VR](https://mlsanity.com/paper/2409.11925/) (2024)
- [3D Dynamics-Aware Manipulation: Endowing Manipulation Policies with 3D Foresight](https://mlsanity.com/paper/2502.10028/) (2025)

## Built on by (most cited first, 4 of 4)

- [TAVIS: A Benchmark for Egocentric Active Vision and Anticipatory Gaze in Imitation Learning](https://mlsanity.com/paper/2605.07943/) (2026)
- [HAP: A Hand-Driven Active Perception Framework for Egocentric Head Motion Prediction](https://mlsanity.com/paper/2609.18548/) (2026)
- [ActiveArena: Benchmarking and Understanding Active Perception in Robotic Manipulation](https://mlsanity.com/paper/2609.24124/) (2026)
- [RoboQuest: Generalist Physical Agents that Search, Inspect and Test](https://mlsanity.com/paper/2610.10388/) (2026)

## Abstract (arXiv)

Recently, active vision has reemerged as an important concept for manipulation, since visual occlusion occurs more frequently when main cameras are mounted on the robot heads. We reflect on the visual occlusion issue and identify its essence as the absence of information useful for task completion. Inspired by this, we come up with the more fundamental problem of Exploratory and Focused Manipulation (EFM). The proposed problem is about actively collecting information to complete challenging manipulation tasks that require exploration or focus. As an initial attempt to address this problem, we establish the EFM-10 benchmark that consists of 4 categories of tasks that align with our definition (10 tasks in total). We further come up with a Bimanual Active Perception (BAP) strategy, which leverages one arm to provide active vision and another arm to provide force sensing while manipulating. Based on this idea, we collect a dataset named BAPData for the tasks in EFM-10. With the dataset, we successfully verify the effectiveness of the BAP strategy in an imitation learning manner. We hope that the EFM-10 benchmark along with the BAP strategy can become a cornerstone that facilitates future research towards this direction. Project website: EFManipulation.github.io.