Reproduce “Reinforcement Learning of Active Vision for Manipulating Objects under Occlusions” on OpenResearch
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About this paper
Ricson Cheng, Arpit Agarwal, Katerina Fragkiadaki
This research introduces a data-driven framework for robots to learn joint hand-eye control, enabling robust object manipulation in cluttered environments with frequent occlusions. The approach demonstrates that actively controlling a camera to maintain visibility of the target object significantly improves manipulation task success compared to static camera systems.
Key insights
- Curriculum learning, progressing from simpler to more complex environments, is essential for successfully training manipulation policies under occlusions.
- Incorporating object-centric attention through a dedicated object detector module is critical for robust learning, outperforming vanilla frame-centric CNN approaches.
- Abstracting the state for the gripper actor (providing only 3D object and gripper locations) improves learning speed and final success rates.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.