Interpretable models for fast activity recognition and anomaly explanation during collaborative robotics tasks
In this paper, we present Rapid Activity Prediction Through Object-oriented Regression (RAPTOR), a scalable method for performing rapid, real-time activity recognition and prediction that achieves state-of-the-art classification accuracy on both a generic human activity dataset and two domain-specif...
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| Format: | Article |
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Institute of Electrical and Electronics Engineers (IEEE)
2018
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| Online Access: | http://hdl.handle.net/1721.1/115395 https://orcid.org/0000-0003-1338-8107 |
| _version_ | 1811091698609029120 |
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| author | Hayes, Bradley H Shah, Julie A |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Hayes, Bradley H Shah, Julie A |
| author_sort | Hayes, Bradley H |
| collection | MIT |
| description | In this paper, we present Rapid Activity Prediction Through Object-oriented Regression (RAPTOR), a scalable method for performing rapid, real-time activity recognition and prediction that achieves state-of-the-art classification accuracy on both a generic human activity dataset and two domain-specific collaborative robotics manufacturing datasets. Our approach is designed to be human-interpretable: able to provide explanations for its reasoning such that non-experts can better understand and improve its activity models. We incorporate methods to increase RAPTOR's resilience against confusion due to temporal variations, as well as against learning false correlations between features. We report full and partial trajectory classification results across three datasets and conclude by demonstrating our model's ability to provide interpretable explanations of its reasoning using outlier detection techniques. |
| first_indexed | 2024-09-23T15:06:32Z |
| format | Article |
| id | mit-1721.1/115395 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T15:06:32Z |
| publishDate | 2018 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/1153952022-09-29T12:45:37Z Interpretable models for fast activity recognition and anomaly explanation during collaborative robotics tasks Hayes, Bradley H Shah, Julie A Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Hayes, Bradley H Shah, Julie A In this paper, we present Rapid Activity Prediction Through Object-oriented Regression (RAPTOR), a scalable method for performing rapid, real-time activity recognition and prediction that achieves state-of-the-art classification accuracy on both a generic human activity dataset and two domain-specific collaborative robotics manufacturing datasets. Our approach is designed to be human-interpretable: able to provide explanations for its reasoning such that non-experts can better understand and improve its activity models. We incorporate methods to increase RAPTOR's resilience against confusion due to temporal variations, as well as against learning false correlations between features. We report full and partial trajectory classification results across three datasets and conclude by demonstrating our model's ability to provide interpretable explanations of its reasoning using outlier detection techniques. 2018-05-16T14:48:59Z 2018-05-16T14:48:59Z 2017-07 2017-05 2018-04-10T16:23:24Z Article http://purl.org/eprint/type/ConferencePaper 978-1-5090-4633-1 http://hdl.handle.net/1721.1/115395 Hayes, Bradley, and Julie A. Shah. “Interpretable Models for Fast Activity Recognition and Anomaly Explanation during Collaborative Robotics Tasks,” 2017 IEEE International Conference on Robotics and Automation (ICRA), 29 May - 3 June, 2017, Singapore, Singapore, 6586–93. IEEE, 2017. © 2017 IEEE https://orcid.org/0000-0003-1338-8107 http://dx.doi.org/10.1109/ICRA.2017.7989778 2017 IEEE International Conference on Robotics and Automation (ICRA) Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) MIT Web Domain |
| spellingShingle | Hayes, Bradley H Shah, Julie A Interpretable models for fast activity recognition and anomaly explanation during collaborative robotics tasks |
| title | Interpretable models for fast activity recognition and anomaly explanation during collaborative robotics tasks |
| title_full | Interpretable models for fast activity recognition and anomaly explanation during collaborative robotics tasks |
| title_fullStr | Interpretable models for fast activity recognition and anomaly explanation during collaborative robotics tasks |
| title_full_unstemmed | Interpretable models for fast activity recognition and anomaly explanation during collaborative robotics tasks |
| title_short | Interpretable models for fast activity recognition and anomaly explanation during collaborative robotics tasks |
| title_sort | interpretable models for fast activity recognition and anomaly explanation during collaborative robotics tasks |
| url | http://hdl.handle.net/1721.1/115395 https://orcid.org/0000-0003-1338-8107 |
| work_keys_str_mv | AT hayesbradleyh interpretablemodelsforfastactivityrecognitionandanomalyexplanationduringcollaborativeroboticstasks AT shahjuliea interpretablemodelsforfastactivityrecognitionandanomalyexplanationduringcollaborativeroboticstasks |