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 agent-environment 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 tracker within an hour on a
consumer-level GPU, e.g., Nvidia RTX 3090. Such efficiency is unprecedented for
RL-based visual tracking methods. We evaluate our tracker 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 tracker from the virtual world to real-world scenarios.

提出了一个结合了视觉基础模型和离线强化学习的新框架，以赋予具有体内视觉的智能体进行视觉跟踪任务，并通过使用蒙版重定向机制和多级数据收集策略进一步提高了跟踪结果的稳健性和泛化性。通过对多种高保真度场景进行的评估，结果表明该方法在样本效率、对干扰物的稳健性和对未见情况和目标的泛化能力方面明显优于现有的方法，并证明了学习到的跟踪器从虚拟世界到真实场景的可迁移性。