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Learning and Recognition of Hybrid Manipulation Motions in Variable Environments Using Probabilistic Flow Tubes

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Abstract

For robots to work effectively with humans, they must learn and recognize activities that humans perform. We enable a robot to learn a library of activities from user demonstrations and use it to recognize an action performed by an operator in real time. Our contributions are threefold: (1) a novel probabilistic flow tube representation that can intuitively capture a wide range of motions and can be used to support compliant execution; (2) a method to identify the relevant features of a motion, and ensure that the learned representation preserves these features in new and unforeseen situations; (3) a fast incremental algorithm for recognizing user-performed motions using this representation. Our approach provides several capabilities beyond those of existing algorithms. First, we leverage temporal information to model motions that may exhibit non-Markovian characteristics. Second, our approach can identify parameters of a motion not explicitly specified by the user. Third, we model hybrid continuous and discrete motions in a unified representation that avoids abstracting out the continuous details of the data. Experimental results show a 49 % improvement over prior art in recognition rate for varying environments, and a 24 % improvement for a static environment, while maintaining average computing times for incremental recognition of less than half of human reaction time. We also demonstrate motion learning and recognition capabilities on real-world robot platforms.

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Acknowledgements

The authors thank David Mittman, Sarah Osentoski, and Shuo Wang for their help in operating the different robots used in our demonstrations and experiments.

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Authors and Affiliations

  1. 362 Memorial Dr. #203, Cambridge, MA, 02139, USA

    Shuonan Dong

  2. 32 Vassar St. Room 32-227, Cambridge, MA, 02139, USA

    Brian Williams

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  1. Shuonan Dong
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  2. Brian Williams
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Correspondence to Shuonan Dong.

Additional information

This research was supported by a National Defense Science and Engineering Graduate (NDSEG) Fellowship, 32 CFR 168a. Additional support was provided by a NASA JPL Strategic University Research Partnership.

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Dong, S., Williams, B. Learning and Recognition of Hybrid Manipulation Motions in Variable Environments Using Probabilistic Flow Tubes. Int J of Soc Robotics 4, 357–368 (2012). https://doi.org/10.1007/s12369-012-0155-x

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  • DOI: https://doi.org/10.1007/s12369-012-0155-x

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