Summary of Reinforcement Learning with Generalizable Gaussian Splatting, by Jiaxu Wang et al.
Reinforcement Learning with Generalizable Gaussian Splatting
by Jiaxu Wang, Qiang Zhang, Jingkai Sun, Jiahang Cao, Gang Han, Wen Zhao, Weining Zhang, Yecheng Shao, Yijie Guo, Renjing Xu
First submitted to arxiv on: 18 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel framework called GSRL (Generalizable Gaussian Splatting) that uses explicit scene representations to improve reinforcement learning (RL) performance, particularly in vision-based tasks. The authors recognize the limitations of previous methods, which rely on implicit or explicit representations like images, points, voxels, and neural radiance fields. These approaches struggle with complex local geometries, generalization to unseen scenes, and require precise foreground masks. In contrast, 3D Gaussian Splatting (3DGS) offers a revolutionary change for reconstruction and representation methods. The proposed GSRL framework builds upon 3DGS, leveraging its differentiable rendering nature to improve RL performance. Experimental results in the RoboMimic environment demonstrate the effectiveness of GSRL, outperforming baselines by 10%, 44%, and 15% on a challenging task. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using new ways to represent environments for reinforcement learning (RL) tasks. Currently, people use images or other methods that can’t handle complex scenes or generalize well. The authors propose a new method called GSRL that uses something called 3D Gaussian Splatting to improve RL performance. They tested this method in a robotic environment and got better results than others. |
Keywords
» Artificial intelligence » Generalization » Reinforcement learning
