Multimodal anomaly detection for assistive robots
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Detecting when something unusual has happened could help assistive robots operate more safely and effectively around people. However, the variability associated with people and objects in human environments can make anomaly detection difficult. We previously introduced an algorithm that uses a hidden Markov model (HMM) with a log-likelihood detection threshold that varies based on execution progress. We now present an improved version of our previous algorithm (HMM-D) and introduce a new algorithm based on Gaussian process regression (HMM-GP). We also present a new and more thorough evaluation of 8 anomaly detection algorithms with force, sound, and kinematic signals collected from a robot closing microwave doors, latching a toolbox, scooping yogurt, and feeding yogurt to able-bodied participants. Overall, HMM-GP had the highest performance in terms of area under the curve for these real-world tasks, and multiple modalities improved performance with some anomalies being better detected with particular modalities. With synthetic anomalies, HMM-D exhibited shorter detection delays and outperformed HMM-GP with high-magnitude anomalies. In general, higher-magnitude synthetic anomalies tended to be detected more rapidly.
KeywordsMultimodality Anomaly detection Assistive manipulation Execution monitoring
We thank Youkeun Kim, Zackory Erickson, Ariel Kapusta, Chansu Kim, and Jane Chisholm for their assistance throughout this project. This work was supported in part by NSF Awards IIS-1150157, EFRI-1137229, and NIDILRR Grant 90RE5016-01-00 via RERC TechSAge. Dr. Kemp is a cofounder, a board member, an equity holder, and the CTO of Hello Robot, Inc., which is developing products related to this research. This research could affect his personal financial status. The terms of this arrangement have been reviewed and approved by Georgia Tech in accordance with its conflict of interest policies.
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