連続SE(3)軌道における能動的目標追跡のための方策学習
Policy Learning for Active Target Tracking over Continuous SE(3) Trajectories
限られた視野のセンサを搭載した移動ロボットが動的目標を追跡するため、目標分布のエントロピーを最小化する連続制御方策をモデルベース方策勾配法で学習する手法を提案。
著者: Pengzhi Yang, Shumon Koga, Arash Asgharivaskasi, Nikolay Atanasov
分類: cs.RO, cs.LG
原文アブストラクト
This paper proposes a novel model-based policy gradient algorithm for tracking dynamic targets using a mobile robot, equipped with an onboard sensor with limited field of view. The task is to obtain a continuous control policy for the mobile robot to collect sensor measurements that reduce uncertainty in the target states, measured by the target distribution entropy. We design a neural network control policy with the robot $SE(3)$ pose and the mean vector and information matrix of the joint target distribution as inputs and attention layers to handle variable numbers of targets. We also derive the gradient of the target entropy with respect to the network parameters explicitly, allowing efficient model-based policy gradient optimization.
