Empowering Embodied Visual Tracking with Visual Foundation Models and Offline RL

• URL: http://arxiv.org/abs/2404.09857v2
• Date: Mon, 22 Jul 2024 06:13:32 GMT
• Title: Empowering Embodied Visual Tracking with Visual Foundation Models and Offline RL
• Authors: Fangwei Zhong, Kui Wu, Hai Ci, Churan Wang, Hao Chen,
• Abstract summary: Embodied visual tracking is a vital and challenging skill for embodied agents. Existing methods suffer from inefficient training and poor generalization. We propose a novel framework that combines visual foundation models and offline reinforcement learning.
• Score: 19.757030674041037
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Embodied visual tracking is to follow a target object in dynamic 3D environments using an agent's egocentric vision. This is a vital and challenging skill for embodied agents. However, existing methods suffer from inefficient training and poor generalization. In this paper, we propose a novel framework that combines visual foundation models(VFM) and offline reinforcement learning(offline RL) to empower embodied visual tracking. We use a pre-trained VFM, such as "Tracking Anything", to extract semantic segmentation masks with text prompts. We then train a recurrent policy network with offline RL, e.g., Conservative Q-Learning, to learn from the collected demonstrations without online interactions. To further improve the robustness and generalization of the policy network, we also introduce a mask re-targeting mechanism and a multi-level data collection strategy. In this way, we can train a robust policy within an hour on a consumer-level GPU, e.g., Nvidia RTX 3090. We evaluate our agent on several high-fidelity environments with challenging situations, such as distraction and occlusion. The results show that our agent outperforms state-of-the-art methods in terms of sample efficiency, robustness to distractors, and generalization to unseen scenarios and targets. We also demonstrate the transferability of the learned agent from virtual environments to a real-world robot.

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