Active Object Tracking (AOT) aims to maintain a specific relation between the tracker and object(s) by autonomously controlling the motion system of a tracker given observations. AOT has wide-ranging applications, such as in mobile robots and autonomous driving. However, building a generalizable active tracker that works robustly across different scenarios remains a challenge, especially in unstructured environments with cluttered obstacles and diverse layouts. We argue that constructing a state representation capable of modeling the geometry structure of the surroundings and the dynamics of the target is crucial for achieving this goal. To address this challenge, we present RSPT, a framework that forms a structure-aware motion representation by Reconstructing the Surroundings and Predicting the target Trajectory. Additionally, we enhance the generalization of the policy network by training in an asymmetric dueling mechanism. We evaluate RSPT on various simulated scenarios and show that it outperforms existing methods in unseen environments, particularly those with complex obstacles and layouts. We also demonstrate the successful transfer of RSPT to real-world settings. Project Website: https://sites.google.com/view/aot-rspt.
翻译:主动物体追踪(AOT)的目标是通过自主控制追踪器的运动系统来维持追踪器和目标之间的特定关系。AOT具有广泛的应用,例如移动机器人和自动驾驶。然而,在杂乱无序的环境中构建一个能够在不同场景中稳定运行的泛化主动追踪器仍然是一项挑战,尤其是那些充满复杂障碍物和多样化布局的环境。我们认为构建一个能够建模周围环境的几何结构和目标动力学的状态表示对于实现此目标至关重要。为了解决这个问题,我们提出了RSPT框架,通过重建环境和预测目标轨迹形成具有结构感知的运动表示。此外,我们通过进行不对称的决斗机制训练,进一步提高了策略网络的泛化能力。我们在各种模拟场景中评估了RSPT的性能,并表明它在未见过的环境中优于现有方法,特别是那些具有复杂障碍和布局的环境。我们还展示了将RSPT成功转移至实际环境。项目网站:https://sites.google.com/view/aot-rspt。