Title:
Multimodal Execution Monitoring for Anomaly Detection During Robot Manipulation
Multimodal Execution Monitoring for Anomaly Detection During Robot Manipulation
Author(s)
Park, Daehyung
Erickson, Zackory
Bhattacharjee, Tapomayukh
Kemp, Charles C.
Erickson, Zackory
Bhattacharjee, Tapomayukh
Kemp, Charles C.
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Abstract
Online detection of anomalous execution can be
valuable for robot manipulation, enabling robots to operate
more safely, determine when a behavior is inappropriate,
and otherwise exhibit more common sense. By using multiple
complementary sensory modalities, robots could potentially
detect a wider variety of anomalies, such as anomalous contact
or a loud utterance by a human. However, task variability
and the potential for false positives make online anomaly
detection challenging, especially for long-duration manipulation
behaviors. In this paper, we provide evidence for the value of
multimodal execution monitoring and the use of a detection
threshold that varies based on the progress of execution. Using
a data-driven approach, we train an execution monitor that
runs in parallel to a manipulation behavior. Like previous
methods for anomaly detection, our method trains a hidden
Markov model (HMM) using multimodal observations from
non-anomalous executions. In contrast to prior work, our
system also uses a detection threshold that changes based on
the execution progress. We evaluated our approach with haptic,
visual, auditory, and kinematic sensing during a variety of manipulation
tasks performed by a PR2 robot. The tasks included
pushing doors closed, operating switches, and assisting ablebodied
participants with eating yogurt. In our evaluations, our
anomaly detection method performed substantially better with
multimodal monitoring than single modality monitoring. It also
resulted in more desirable ROC curves when compared with
other detection threshold methods from the literature, obtaining
higher true positive rates for comparable false positive rates.
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Date Issued
2016-05
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Proceedings