Summary of Empowering Embodied Visual Tracking with Visual Foundation Models and Offline Rl, by Fangwei Zhong et al.
Empowering Embodied Visual Tracking with Visual Foundation Models and Offline RL
by Fangwei Zhong, Kui Wu, Hai Ci, Churan Wang, Hao Chen
First submitted to arxiv on: 15 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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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 This paper proposes a novel framework for embodied visual tracking in dynamic 3D environments using an agent’s egocentric vision. The approach combines visual foundation models (VFM) and offline reinforcement learning (offline RL) to empower the tracking process. Specifically, it uses pre-trained VFM like “Tracking Anything” to extract semantic segmentation masks with text prompts, and then trains a recurrent policy network with offline RL algorithms like Conservative Q-Learning to learn from collected demonstrations without online interactions. To improve robustness and generalization, the framework introduces a mask re-targeting mechanism and a multi-level data collection strategy. This allows for efficient training on consumer-level GPUs, such as Nvidia RTX 3090, within an hour. The paper evaluates the agent’s performance on high-fidelity environments with challenging situations like distraction and occlusion, showing it outperforms state-of-the-art methods in terms of sample efficiency, robustness to distractors, and generalization to unseen scenarios and targets. The framework also demonstrates transferability from virtual environments to real-world robots. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research is about creating a computer program that can follow objects in 3D spaces using its own point of view. This is an important skill for robots or other artificial agents. Current methods have limitations, such as needing too much training data and not being able to handle complex situations. The new approach uses pre-trained models to identify objects and then teaches the agent how to track them without needing to interact with the environment directly. This allows for efficient learning on regular computers within an hour. The researchers tested their program on various simulated environments, showing it can perform better than current methods in terms of speed, adaptability, and ability to handle unexpected situations. Additionally, they demonstrated that the learned skills can be applied to real-world robots. |
Keywords
» Artificial intelligence » Generalization » Mask » Reinforcement learning » Semantic segmentation » Tracking » Transferability
