Learning Continuous Control Policies for Information-Theoretic Active Perception
Pengzhi Yang,Yuhan Liu,Shumon Koga,Arash Asgharivaskasi,Nikolay Atanasov,Pengzhi Yang,Yuhan Liu,Shumon Koga,Arash Asgharivaskasi,Nikolay Atanasov
This paper proposes a method for learning continuous control policies for exploration and active landmark localization. We consider a mobile robot detecting landmarks within a limited sensing range, and tackle the problem of learning a control policy that maximizes the mutual information between the landmark states and the sensor observations. We employ a Kalman filter to convert the partially obs...
