POMDP-based action planning for the recognition of occluded objects with Humanoid robots

被引：0

作者：

Tsuru, Masato ^{[1
]}

Gergondet, Pierre ^{[2
]}

Motoda, Tomohiro ^{[1
]}

Escande, Adrien ^{[2
]}

Yoshida, Eiichi ^{[3
]}

Ramirez-Alpizar, Ixchel G. ^{[4
]}

Wan, Weiwei ^{[1
]}

Harada, Kensuke ^{[5
]}

机构：

[1] Osaka Univ, Grad Sch Engn Sci, 1-3 Machikaneyama, Osaka, Japan

[2] AIST, CNRS JRL, 1-1-1 Umezono, Tsukuba, Ibaraki, Japan

[3] AIST, Intelligent Syst Res Inst, 1-1-1 Umezono, Tsukuba, Ibaraki, Japan

[4] AIST, AIRC, Koto Ku, 2-3-26 Aomi, Tokyo, Japan

[5] Osaka Univ, Grad Sch Engn Sci, AIRC, AIST, Osaka, Japan

来源：

PROCEEDINGS OF THE 2020 INTERNATIONAL CONFERENCE ON ARTIFICIAL LIFE AND ROBOTICS (ICAROB2020) | 2020年

关键词：

humanoid robot; POMDP; planning; point cloud; occlusion; 6DoF registration;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：281 / 284

页数：4

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