# RGBManip: Monocular Image-based Robotic Manipulation through Active Object Pose Estimation

arXiv: https://arxiv.org/abs/2310.03478 · HTML: https://mlsanity.com/paper/2310.03478/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Oct 2023.

## Built on by (most cited first, 14 of 14)

- [Real2Code: Reconstruct Articulated Objects via Code Generation](https://mlsanity.com/paper/2406.08474/) (2024)
- [Omni6DPose: A Benchmark and Model for Universal 6D Object Pose Estimation and Tracking](https://mlsanity.com/paper/2406.04316/) (2024)
- [Lift3D Foundation Policy: Lifting 2D Large-Scale Pretrained Models for Robust 3D Robotic Manipulation](https://mlsanity.com/paper/2411.18623/) (2024)
- [Perceiving and Acting in First-Person: A Dataset and Benchmark for Egocentric Human-Object-Human Interactions](https://mlsanity.com/paper/2508.04681/) (2025)
- [CrayonRobo: Object-Centric Prompt-Driven Vision-Language-Action Model for Robotic Manipulation](https://mlsanity.com/paper/2505.02166/) (2025)
- [GAPartManip: A Large-scale Part-centric Dataset for Material-Agnostic Articulated Object Manipulation](https://mlsanity.com/paper/2411.18276/) (2024)
- [Articulated Object Manipulation using Online Axis Estimation with SAM2-Based Tracking](https://mlsanity.com/paper/2409.16287/) (2024)
- [Ensuring Force Safety in Vision-Guided Robotic Manipulation via Implicit Tactile Calibration](https://mlsanity.com/paper/2412.10349/) (2024)
- [Observer-Actor: Active Vision Imitation Learning with Sparse-View Gaussian Splatting](https://mlsanity.com/paper/2511.18140/) (2025)
- [VAPO: Visibility-Aware Keypoint Localization for Efficient 6DoF Object Pose Estimation](https://mlsanity.com/paper/2403.14559/) (2024)
- [Acquisition of high-quality images for camera calibration in robotics applications via speech prompts](https://mlsanity.com/paper/2504.11031/) (2025)
- [Watch Less, Feel More: Sim-to-Real RL for Generalizable Articulated Object Manipulation via Motion Adaptation and Impedance Control](https://mlsanity.com/paper/2502.14457/) (2025)
- [Hierarchical Audio-Visual-Proprioceptive Fusion for Precise Robotic Manipulation](https://mlsanity.com/paper/2602.13640/) (2026)
- [SenseWeather: Multimodal Sensing for Robust Robotic Perception under Extreme Conditions](https://mlsanity.com/paper/2610.14082/) (2026)

## Abstract (arXiv)

Robotic manipulation requires accurate perception of the environment, which poses a significant challenge due to its inherent complexity and constantly changing nature. In this context, RGB image and point-cloud observations are two commonly used modalities in visual-based robotic manipulation, but each of these modalities have their own limitations. Commercial point-cloud observations often suffer from issues like sparse sampling and noisy output due to the limits of the emission-reception imaging principle. On the other hand, RGB images, while rich in texture information, lack essential depth and 3D information crucial for robotic manipulation. To mitigate these challenges, we propose an image-only robotic manipulation framework that leverages an eye-on-hand monocular camera installed on the robot's parallel gripper. By moving with the robot gripper, this camera gains the ability to actively perceive object from multiple perspectives during the manipulation process. This enables the estimation of 6D object poses, which can be utilized for manipulation. While, obtaining images from more and diverse viewpoints typically improves pose estimation, it also increases the manipulation time. To address this trade-off, we employ a reinforcement learning policy to synchronize the manipulation strategy with active perception, achieving a balance between 6D pose accuracy and manipulation efficiency. Our experimental results in both simulated and real-world environments showcase the state-of-the-art effectiveness of our approach. %, which, to the best of our knowledge, is the first to achieve robust real-world robotic manipulation through active pose estimation. We believe that our method will inspire further research on real-world-oriented robotic manipulation.