Efficient Multi-scale POMDPs for Robotic Object Search and Delivery

Holzherr, Luc; Förster, Julian; Breyer, Michel; Nieto, Juan; Siegwart, Roland; Chung, Jen Jen

doi:10.1109/icra48506.2021.9561047

Cited by 5 publications

(2 citation statements)

References 13 publications

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“…Wixson and Ballard [28] remarks that selecting views for object search in a local region is a harder problem than the selection of which region to search in. Most works that demonstrate robotic search within a search region reduce the problem to 2D [29,6,30,7,31,32,33,34]. For a literature survey and taxonomy of object search, refer to [12].…”

Section: Related Workmentioning

confidence: 99%

A System for Generalized 3D Multi-Object Search

Zheng¹,

Anirudha²,

Tellex³

2023

Preprint

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Searching for objects is a fundamental skill for robots. As such, we expect object search to eventually become an off-the-shelf capability for robots, similar to e.g., object detection and SLAM. In contrast, however, no system for 3D object search exists that generalizes across real robots and environments. In this paper, building upon a recent theoretical framework that exploited the octree structure for representing belief in 3D, we present GenMOS (Generalized Multi-Object Search), the first general-purpose system for multiobject search (MOS) in a 3D region that is robot-independent and environment-agnostic. GenMOS takes as input point cloud observations of the local region, object detection results, and localization of the robot's view pose, and outputs a 6D viewpoint to move to through online planning. In particular, GenMOS uses point cloud observations in three ways: (1) to simulate occlusion; (2) to inform occupancy and initialize octree belief; and (3) to sample a belief-dependent graph of view positions that avoid obstacles. We evaluate our system both in simulation and on two real robot platforms. Our system enables, for example, a Boston Dynamics Spot robot to find a toy cat hidden underneath a couch in under one minute. We further integrate 3D local search with 2D global search to handle larger areas, demonstrating the resulting system in a 25m 2 lobby area.

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Section: Related Workmentioning

confidence: 99%

A System for Generalized 3D Multi-Object Search

Zheng¹,

Anirudha²,

Tellex³

2023

Preprint

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show abstract

“…In above two researches, the target location is specific, the research contents focus on autonomous motion planning. A novel hierarchical partially observable Markov decision process framework (Holzherr et al , 2021) with belief state of object in possible locations is proposed to solve robot search and delivery tasks. Robot search task is solved through the goal uncertain stochastic shortest path problem (Saisubramanian et al , 2019) to obtain path planning and decision-making.…”

Section: Introductionmentioning

confidence: 99%

Path planning for robot search task using belief criteria decision-making

Zhao,

Liu,

et al. 2024

RIA

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Purpose This study aims to realize efficient, fast and safe robot search task, the belief criteria decision-making approach is proposed to solve the object search task with an uncertain location. Design/methodology/approach The study formulates the robot search task as a partially observable Markov decision process, uses the semantic information to evaluate the belief state and designs the belief criteria decision-making approach. A cost function considering a trade-off among belief state, path length and movement effort is modelled to select the next best location in path planning. Findings The semantic information is successfully modelled and propagated, which can represent the belief of finding object. The belief criteria decision-making (BCDM) approach is evaluated in both Gazebo simulation platform and physical experiments. Compared to greedy, uniform and random methods, the performance index of path length and execution time is superior by BCDM approach. Originality/value The prior knowledge of robot working environment, especially semantic information, can be used for path planning to achieve efficient task execution in path length and execution time. The modelling and updating of environment information can lead a promising research topic to realize a more intelligent decision-making method for object search task.

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Domain Adaptation in Reinforcement Learning: Approaches, Limitations, and Future Directions

Wang

2024

J. Inst. Eng. India Ser. B

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Efficient Multi-scale POMDPs for Robotic Object Search and Delivery

Cited by 5 publications

References 13 publications

A System for Generalized 3D Multi-Object Search

A System for Generalized 3D Multi-Object Search

Path planning for robot search task using belief criteria decision-making

Domain Adaptation in Reinforcement Learning: Approaches, Limitations, and Future Directions

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