Efficient Planning in Non-Gaussian Belief Spaces and Its Application to Robot Grasping
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- 刊名：Springer Tracts in Advanced Robotics
- 出版年：2017
- 出版时间：2017
- 年：2017
- 卷：100
- 期：1
- 页码：253-269
- 全文大小：658 KB
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Leslie Kaelbling (5)
Tomas Lozano-Perez (5)
Russ Tedrake (5)
5. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 32 Vassar St., Cambridge, MA, USA - 丛书名：Robotics Research
- ISBN：978-3-319-29363-9
- 刊物类别：Engineering
- 刊物主题：Automation and Robotics
Control Engineering - 出版者：Springer Berlin / Heidelberg
- ISSN：1610-742X
- 卷排序：100
文摘
The limited nature of robot sensors make many important robotics problems partially observable. These problems may require the system to perform complex information-gathering operations. One approach to solving these problems is to create plans in belief-space, the space of probability distributions over the under-lying state of the system. The belief-space plan encodes a strategy for performing a task while gaining information as necessary. Most approaches to belief-space planning rely upon representing belief state in a particular way (typically as a Gaussian). Unfortunately, this can lead to large errors between the assumed density representation of belief state and the true belief state. This paper proposes a new sample-based approach to belief-space planning that has fixed computational complexity while allowing arbitrary implementations of Bayes filtering to be used to track belief state. The approach is illustrated in the context of a simple example and compared to a prior approach. Then, we propose an application of the technique to an instance of the grasp synthesis problem where a robot must simultaneously localize and grasp an object given initially uncertain object parameters by planning information-gathering behavior. Experimental results are presented that demonstrate the approach to be capable of actively localizing and grasping boxes that are presented to the robot in uncertain and hard-to-localize configurations.