Nonparametric Bayesian Method for Robot Anomaly Monitoring

Zhou, Xuefeng; Wu, Hongmin; Rojas, Juan; Xu, Zhihao; Li, Shuai

doi:10.1007/978-981-15-6263-1_4

Cited by 3 publications

(1 citation statement)

References 27 publications

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“…Among recent works, [30] extends planning with a visionbased execution monitoring system, [31] analyzes different preprocessing techniques for introspective data to detect gearbox failures. Non-parametric Bayesian models [32] and Non-parametric Hidden Markov Models (HMMs) [33] are investigated to detect and classify anomalies.…”

Section: Related Workmentioning

confidence: 99%

FINO-Net: A Deep Multimodal Sensor Fusion Framework for Manipulation Failure Detection

İnceoğlu

Aksoy

et al. 2021

2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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An autonomous service robot should be able to interact with its environment safely and robustly without requiring human assistance. Unstructured environments are challenging for robots since the exact prediction of outcomes is not always possible. Even when the robot behaviors are well-designed, the unpredictable nature of physical robotobject interaction may prevent success in object manipulation. Therefore, execution of a manipulation action may result in an undesirable outcome involving accidents or damages to the objects or environment. Situation awareness becomes important in such cases to enable the robot to (i) maintain the integrity of both itself and the environment, (ii) recover from failed tasks in the short term, and (iii) learn to avoid failures in the long term. For this purpose, robot executions should be continuously monitored, and failures should be detected and classified appropriately. In this work, we focus on detecting and classifying both manipulation and post-manipulation phase failures using the same exteroception setup. We cover a diverse set of failure types for primary tabletop manipulation actions. In order to detect these failures, we propose FINO-Net [1], a deep multimodal sensor fusion based classifier network. Proposed network accurately detects and classifies failures from raw sensory data without any prior knowledge. In this work, we use our extended FAILURE dataset [1] with 99 new multimodal manipulation recordings and annotate them with their corresponding failure types. FINO-Net achieves 0.87 failure detection and 0.80 failure classification F1 scores. Experimental results show that proposed architecture is also appropriate for real-time use.

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