Active Object Tracking (AOT) aims at keeping a specific relation between tracker and object(s) by autonomously controlling the motion system of a tracker given observations. It is an important research area widely used in various applications, such as mobile robots, intelligent city, autonomous driving, etc. However, it remains challenging to build a generalizable active tracker to robustly work across different scenarios, especially in those unstructured environments with cluttered obstacles and diverse layouts. To realize this, we argue that the key is to construct a state representation that can model the geometry structure of the surroundings and the dynamics of the target. To this end, we propose a framework to form a structure-aware motion representation by Reconstructing the Surroundings of the tracker and Predicting the Trajectory of the target, called RSPT. Moreover, we further enhance the generalization of the policy network by training in the asymmetric dueling mechanism. Empirical results in virtual environments show that the tracker with RSPT significantly outperforms the existing methods among unseen environments, especially in environments with cluttered obstacles and diverse layouts. We further deploy the RSPT in a real-world scenario, showing good generalization in sim-to-real transfer.

Towards Generalizable Active Object Tracking with Reconstructing Surroundings and Predicting Trajectory

CV: Vision for Robotics & Autonomous Driving 2

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AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

Fangwei Zhong

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Towards Generalizable Active Object Tracking with Reconstructing Surroundings and Predicting Trajectory

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