HARPS: An Online POMDP Framework for Human-Assisted Robotic Planning and Sensing

Burks, Luke; Ray, Hunter M.; McGinley, Jamison; Vunnam, Sousheel; Ahmed, Nisar

doi:10.1109/tro.2023.3263460

Cited by 3 publications

(2 citation statements)

References 47 publications

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“…However, the observation model must proficiently capture human reliability without imposing a computational burden while delivering accurate information. Research on human drawing information fusion is conducted within robotics applications [16]- [18]. The authors of [16] illustrate humans conveying map data via hand-sketched drawings for robot navigation, however, neglect inherent human-provided information uncertainty.…”

Section: Introductionmentioning

confidence: 99%

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Online Target Localization Using Adaptive Belief Propagation in the HMM Framework

Seo

Kia

2022

IEEE Robot. Autom. Lett.

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This paper considers a human-autonomy collaborative sensor data fusion for dynamic target localization in a Bayesian framework. To compensate for the shortcomings of an autonomous tracking system, we propose to collect spatial sensing information from human operators who visually monitor the target and can provide target localization information in the form of free sketches encircling the area where the target is located. Our focus in this paper is to construct an adaptive probabilistic model for human-provided inputs where the adaption terms capture the level of reliability of the human inputs. The next contribution of this paper is a novel joint Bayesian learning method to fuse human and autonomous sensor inputs in a manner that the dynamic changes in human detection reliability are also captured and accounted for. A unique aspect of this Bayesian modeling framework is its analytical closed-form update equations, endowing our method with significant computational efficiency. Simulations demonstrate our results.

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Section: Introductionmentioning

confidence: 99%

“…In contrast, [17] presents a human sketch-based method for static target search with uncertainty, but it doesn't consider the computational costs of parameter updates. The authors of [18] place their primary emphasis on online planning using human drawing observations, rather than focusing on the updating of human parameters.…”

Section: Introductionmentioning

confidence: 99%

Online Target Localization Using Adaptive Belief Propagation in the HMM Framework

Seo

Kia

2022

IEEE Robot. Autom. Lett.

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Human-Centered Autonomy for UAS Target Search

Ray,

Laouar,

Sunberg

et al. 2024

2024 IEEE International Conference on Robotics and Automation (ICRA)

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Advanced Neural Classifier-Based Effective Human Assistance Robots Using Comparable Interactive Input Assessment Technique

Albekairi,

Kaaniche,

Abbas

et al. 2024

Mathematics

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The role of robotic systems in human assistance is inevitable with the bots that assist with interactive and voice commands. For cooperative and precise assistance, the understandability of these bots needs better input analysis. This article introduces a Comparable Input Assessment Technique (CIAT) to improve the bot system’s understandability. This research introduces a novel approach for HRI that uses optimized algorithms for input detection, analysis, and response generation in conjunction with advanced neural classifiers. This approach employs deep learning models to enhance the accuracy of input identification and processing efficiency, in contrast to previous approaches that often depended on conventional detection techniques and basic analytical methods. Regardless of the input type, this technique defines cooperative control for assistance from previous histories. The inputs are cooperatively validated for the instruction responses for human assistance through defined classifications. For this purpose, a neural classifier is used; the maximum possibilities for assistance using self-detected instructions are recommended for the user. The neural classifier is divided into two categories according to its maximum comparable limits: precise instruction and least assessment inputs. For this purpose, the robot system is trained using previous histories and new assistance activities. The learning process performs comparable validations between detected and unrecognizable inputs with a classification that reduces understandability errors. Therefore, the proposed technique was found to reduce response time by 6.81%, improve input detection by 8.73%, and provide assistance by 12.23% under varying inputs.

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HARPS: An Online POMDP Framework for Human-Assisted Robotic Planning and Sensing

Cited by 3 publications

References 47 publications

Online Target Localization Using Adaptive Belief Propagation in the HMM Framework

Online Target Localization Using Adaptive Belief Propagation in the HMM Framework

Human-Centered Autonomy for UAS Target Search

Advanced Neural Classifier-Based Effective Human Assistance Robots Using Comparable Interactive Input Assessment Technique

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