@InProceedings{tsuru:icarob:2019,
  author    = {Tsuru, Masato and Gergondet, Pierre and Motoda, Tomohiro and Escande, Adrien and Yoshida, Eiichi and Ramirez-Alpizar, Ixchel and Wan, Weiwei and Harada, Kensuke},
  title     = {POMDP-based action planning for the recognition of occluded objects with Humanoid robots},
  booktitle = {International Conference on Artificial Life and Robotics},
  year      = {2019},
  pages     = {281--284},
  address   = {Oita, Japan},
  month     = {January 10-January 13},
  keywords  = {humanoid robot, POMDP, planning, point cloud, occlusion, 6DoF registration},
  doi       = {10.5954/ICAROB.2020.OS7-2},
  abstract  = {In this paper, we present a high-layer motion planner which plans humanoid robot actions to search for object models in the robot workspace. To overcome the occlusion problem, our proposed method plans to get different perspectives of the object. POMDP (Partially Observable Markov Decision Process) is used to determine the observation pose of a robot. The planner then builds a comprehensive point cloud of by merging the point clouds gathered from different positions. The point cloud is compared to a 3D model of the object to estimate the pose of the real object.}
}