Grants per year
Personal profile
Research Interests
Professor Argall’s research lies at the intersection of artificial intelligence, rehabilitation robotics and machine learning. A distinguishing theme present within many of her lab's projects is that the machine automation is customizable---to a user's physical abilities, personal preferences or even financial means. A fundamental question that arises time and again throughout many of their projects is how exactly to share control between the robot and the human user, and how to adapt these control formulations over time as the human changes. They are working with a range of hardware platforms, from a smart wheelchair to assistive robotic arms. Robots already synthetically sense, act in and reason about the world, and these technologies can be leveraged to help bridge the gap left by sensory, motor or cognitive impairments in the users of assistive machines.
Education/Academic qualification
Robotics, PhD, Carnegie Mellon University
… → 2009
Robotics, MS, Carnegie Mellon University
… → 2006
Mathematical Sciences, BS, Carnegie Mellon University
Research interests
- Efficient behavior development
- Machine learning techniques
- Robot autonomy
- Robot motion control
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Grants
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CPS: Medium: Information based Control of Cyber-Physical Systems operating in uncertain environments
Argall, B. D. & Murphey, T. D.
9/15/18 → 8/31/21
Project: Research project
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Human and Machine Learning for Customized Control of Assitive Robots
Shirley Ryan AbilityLab, National Institutes of Health
9/1/18 → 5/31/23
Project: Research project
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CAREER: Robot Learning from Motor-Impaired Instructors and Task Partners
Rehabilitation Institute of Chicago, National Science Foundation
2/1/16 → 1/31/21
Project: Research project
Research Output
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Active Intent Disambiguation for Shared Control Robots
Gopinath, D. E. & Argall, B. D., Jun 2020, In: IEEE Transactions on Neural Systems and Rehabilitation Engineering. 28, 6, p. 1497-1506 10 p., 9066939.Research output: Contribution to journal › Article › peer-review
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Data-driven Koopman operators for model-based shared control of human–machine systems
Broad, A., Abraham, I., Murphey, T. & Argall, B., Aug 1 2020, In: International Journal of Robotics Research. 39, 9, p. 1178-1195 18 p.Research output: Contribution to journal › Article › peer-review
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Discrete N-Dimensional Entropy of Behavior: DNDEB
Young, M., Javaremi, M. N. & Argall, B. D., Nov 2019, 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019. Institute of Electrical and Electronics Engineers Inc., p. 2227-2233 7 p. 8968600. (IEEE International Conference on Intelligent Robots and Systems).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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Formalized task characterization for human-robot autonomy allocation
Young, M., Miller, C., Bi, Y., Chen, W. & Argall, B. D., May 2019, 2019 International Conference on Robotics and Automation, ICRA 2019. Institute of Electrical and Electronics Engineers Inc., p. 6044-6050 7 p. 8793475. (Proceedings - IEEE International Conference on Robotics and Automation; vol. 2019-May).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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Interface Operation and Implications for Shared-Control Assistive Robots
Javaremi, M. N., Young, M. & Argall, B. D., Jun 1 2019, In: IEEE ... International Conference on Rehabilitation Robotics : [proceedings]. 2019, p. 232-239 8 p.Research output: Contribution to journal › Article › peer-review