Online Planning for Target Object Search in Clutter under Partial Observability
| Autor: | Yuchen Xiao, Christopher Amato, Andreas ten Pas, Shengjian Chen, Sammie Katt |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Robot kinematics Computer science business.industry Partially observable Markov decision process Robotics Observable 02 engineering and technology Object (computer science) Task (project management) 020901 industrial engineering & automation 0202 electrical engineering electronic engineering information engineering Task analysis Robot 020201 artificial intelligence & image processing Observability Artificial intelligence business |
| Zdroj: | ICRA |
| DOI: | 10.1109/icra.2019.8793494 |
| Popis: | The problem of finding and grasping a target object in a cluttered, uncertain environment, target object search, is a common and important problem in robotics. One key challenge is the uncertainty of locating and recognizing each object in a cluttered environment due to noisy perception and occlusions. Furthermore, the uncertainty in localization makes manipulation difficult and uncertain. To cope with these challenges, we formulate the target object search task as a partially observable Markov decision process (POMDP), enabling the robot to reason about perceptual and manipulation uncertainty while searching. To further address the manipulation difficulty, we propose Parameterized Action Partially Observable Monte-Carlo Planning (PA-POMCP), an algorithm that evaluates manipulation actions by taking into account the effect of the robot’s current belief on the success of the action execution. In addition, a novel run-time initial belief generator and a state value estimator are introduced in this paper to facilitate the PA-POMCP algorithm. Our experiments show that our methods solve the target object search task in settings where simpler methods either take more object movements or fail. |
| Databáze: | OpenAIRE |
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