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This paper proposes a novel model-based policy gradient algorithm fortracking dynamic targets using a mobile robot, equipped with an onboard sensorwith limited field of view. The task is to obtain a continuous control policyfor the mobile robot to collect sensor measurements that reduce uncertainty inthe target states, measured by the target distribution entropy. We design aneural network control policy with the robot $SE(3)$ pose and the mean vectorand information matrix of the joint target distribution as inputs and attentionlayers to handle variable numbers of targets. We also derive the gradient ofthe target entropy with respect to the network parameters explicitly, allowingefficient model-based policy gradient optimization.

Policy Learning for Active Target Tracking over Continuous SE(3) Trajectories