Reaper: Articulated Object 6d Pose Estimation with Deep Reinforcement Learning | IEEE Conference Publication | IEEE Xplore

Reaper: Articulated Object 6d Pose Estimation with Deep Reinforcement Learning


Abstract:

Current articulated object pose estimation methods largely rely on dense prediction for all the input observed point cloud that suffers from huge computational costs and ...Show More
Notes: This DOI was registered to an article that was not presented by the author(s) at this conference. As per section 8.2.1.B.13 of IEEE's "Publication Services and Products Board Operations Manual," IEEE has chosen to exclude this article from distribution. We regret any inconvenience.

Abstract:

Current articulated object pose estimation methods largely rely on dense prediction for all the input observed point cloud that suffers from huge computational costs and inference time. Besides, self-occlusion is also becoming a key problem that limits the pose estimation performance for those child parts. To solve these issues, we propose a Reinforcement learning based Articulation Pose EstimatoR (ReAPER), which integrates RL into deep neural network for per-part pose estimation. Specifically, we design the novel action space that involves the object’s rotation and translation, as well as a reward function considering chamfer distance during pose fitting. To speed up the RL policy training, we employ imitation learning for policy initialization. Finally, we also introduce a new kinematic energy function to optimize the child parts’ poses. Experimental results show that ReAPER could obtain state-of-the-art performance on articulated object pose estimation task.
Notes: This DOI was registered to an article that was not presented by the author(s) at this conference. As per section 8.2.1.B.13 of IEEE's "Publication Services and Products Board Operations Manual," IEEE has chosen to exclude this article from distribution. We regret any inconvenience.
Date of Conference: 08-11 October 2023
Date Added to IEEE Xplore: 11 September 2023
ISBN Information:
Conference Location: Kuala Lumpur, Malaysia

References

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