Discussion of “A Review of Intent Detection, Arbitration, and Communication Aspects of Shared Control for Physical Human–Robot Interaction” (Losey, D. P., McDonald, C. G., Battaglia, E., and O'Malley, M. K., 2018, ASME Appl. Mech. Rev., 70(1), p. 010804)

[+] Author and Article Information
James P. Schmiedeler

Fellow ASME
Department of Aerospace and Mechanical Engineering,
University of Notre Dame,
Notre Dame, IN 46556
e-mail: schmiedeler.4@nd.edu

Patrick M. Wensing

Department of Aerospace and Mechanical Engineering,
University of Notre Dame,
Notre Dame, IN 46556
e-mail: pwensing@nd.edu

Manuscript received January 9, 2018; final manuscript received January 24, 2018; published online February 14, 2018. Editor: Harry Dankowicz.

Appl. Mech. Rev 70(1), 015503 (Feb 14, 2018) (3 pages) Paper No: AMR-18-1004; doi: 10.1115/1.4039146 History: Received January 09, 2018; Revised January 24, 2018

A unifying description of the shared control architecture within the field of physical human–robot interaction (pHRI) facilitates the education of those being introduced to the field and the framing of new contributions to it. The authors' review of shared control within pHRI proposes such a unifying framework composed of three pillars. First, intent detection addresses the robot's interpretation of human goals, representing one-way communication. Second, arbitration manages the respective roles of the human and robot in the shared control. Third, feedback is the mechanism by which the robot returns information to the human, representing one-way communication in the opposite direction. Interpreting existing contributions through the lens of this framework brings out the importance of mechanical design, modeling, and state-based control.

Copyright © 2018 by ASME
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Computing Community Consortium, 2016, “ A Roadmap for US Robotics: From Internet to Robotics,” Computing Community Consortium, Washington, DC, accessed Feb. 5, 2018, http://jacobsschool.ucsd.edu/contextualrobotics/docs/rm3-final-rs.pdf
SPARC Robotics, 2016, “ Robotics 2020 Multi-Annual Roadmap for Robotics in Europe,” SPARC Robotics, EU-Robotics AISBL, The Hauge, The Netherlands, accessed Feb. 5, 2018, https://www.eu-robotics.net/sparc/upload/about/files/H2020-Robotics-Multi-Annual-Roadmap-ICT-2016.pdf
Haddadin, S. , and Croft, E. , 2016, “ Physical Human-Robot Interaction,” Springer Handbook of Robotics, Springer International Publishing, Berlin, pp. 1835–1874. [CrossRef]
Losey, D. P. , McDonald, C. G. , Battaglia, E. , and O'Malley, M. K. , 2018, “ Review of Intent Detection, Arbitration, and Communication Aspects of Shared Control for Physical Human-Robot Interaction,” ASME Appl. Mech. Rev., 70(1), p. 010804.
Pehlivan, A. U. , Losey, D. P. , and O'Malley, M. K. , 2016, “ Minimal Assist-as-Needed Controller for Upper Limb Robotic Rehabilitation,” IEEE Trans. Rob., 32(1), pp. 113–124. [CrossRef]
Varol, H. A. , Sup, F. , and Goldfarb, M. , 2010, “ Multiclass Real-Time Intent Recognition of a Powered Lower Limb Prosthesis,” IEEE Trans. Biomed. Eng., 57(3), pp. 542–551. [CrossRef] [PubMed]
Young, A. J. , Simon, A. , and Hargrove, L. J. , 2013, “ An Intent Recognition Strategy for Transfemoral Amputee Ambulation Across Different Locomotion Modes,” International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Osaka, Japan, July 3–7, pp. 1587–1590.
Aarno, D. , Ekvall, S. , and Kragic, D. , 2005, “ Adaptive Virtual Fixtures for Machine-Assisted Teleoperation Tasks,” IEEE International Conference on Robotics and Automation (ICRA), Barcelona, Spain, Apr. 18–22, pp. 1139–1144.
Geyer, H. , Seyfarth, A. , and Blickhan, R. , 2006, “ Compliant Leg Behaviour Explains Basic Dynamics of Walking and Running,” R. Soc. B: Biol. Sci., 273(1603), pp. 2861–2867. [CrossRef]
Liu, Y. , Wensing, P. M. , Orin, D. E. , and Zheng, Y. F. , 2015, “ Dynamic Walking in a Humanoid Robot Based on a 3D Actuated Dual-SLIP Model,” IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA, May 26–30, pp. 5710–5717.
Wensing, P. M. , and Revzen, S. , 2017, “ Template Models for Control,” Bioinspired Legged Locomotion, M. Sharbafi and A. Seyfarth , eds., Elsevier, Oxford, UK.
McMullen, D. P. , Hotson, G. , Katyal, K. D. , Wester, B. A. , Fifer, M. S. , McGee, T. G. , Harris, A. , Johannes, M. S. , Vogelstein, R. J. , Ravitz, A. D. , Anderson, W. S. , Thakor, N. V. , and Crone, N. E. , 2014, “ Demonstration of a Semi-Autonomous Hybrid Brain-Machine Interface Using Human Intracranial EEG, Eye Tracking, and Computer Vision to Control a Robotic Upper Limb Prosthetic,” IEEE Trans. Neural Syst. Rehabil. Eng., 22(4), pp. 784–796. [CrossRef] [PubMed]
Wakita, K. , Huang, J. , Di, P. , Sekiyama, K. , and Fukuda, T. , 2013, “ Human-Walking-Intention-Based Motion Control of an Omnidirectional-Type Cane Robot,” IEEE/ASME Trans. Mechatronics, 18(1), pp. 285–296. [CrossRef]
Shultz, A. H. , Lawson, B. E. , and Goldfarb, M. , 2015, “ Running With a Powered Knee and Ankle Prosthesis,” IEEE Trans. Neural Syst. Rehabil. Eng., 23(3), pp. 403–412. [PubMed]
Zeilig, G. , Weingarden, H. , Zwecker, M. , Dudkiewicz, I. , Bloch, A. , and Esquenazi, A. , 2012, “ Safety and Tolerance of the ReWalk Exoskeleton Suit for Ambulation by People With Complete Spinal Cord Injury: A Pilot Study,” J. Spinal Cord Med., 35(2), pp. 96–101. [CrossRef] [PubMed]
Quintero, D. , Martin, A. E. , and Gregg, R. D. , 2017, “ Toward Unified Control of a Powered Prosthetic Leg: A Simulation Study,” IEEE Trans. Control Syst. Technol., 26(1), pp. 305–312. [CrossRef] [PubMed]
Asbeck, A. T. , De Rossi, S. M. , Galiana, I. , Ding, Y. , and Walsh, C. J. , 2014, “ Stronger, Smarter, Softer: Next-Generation Wearable Robots,” IEEE Rob. Autom. Mag., 21(4), pp. 22–33. [CrossRef]
Evrard, P. , and Kheddar, A. , 2009, “ Homotopy Switching Model for Dyad Haptic Interaction in Physical Collaborative Tasks,” World Haptics, Third Joint EuroHaptics Conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems, Salt Lake City, UT, Mar. 18–20, pp. 45–50.
Medina, J. R. , Lorenz, T. , and Hirche, S. , 2015, “ Synthesizing Anticipatory Haptic Assistance Considering Human Behavior Uncertainty,” IEEE Trans. Rob., 31(1), pp. 180–190. [CrossRef]
Hoffman, G. , and Breazeal, C. , 2007, “ Cost-Based Anticipatory Action Selection for Human-Robot Fluency,” IEEE Trans. Rob., 23(5), pp. 952–961. [CrossRef]
Beetz, M. , Stulp, F. , Esden-Tempski, P. , Fedrizzi, A. , Klank, U. , Kresse, I. , Maldonado, A. , and Ruiz, F. , 2010, “ Generality and Legibility in Mobile Manipulation,” Auton. Rob., 28(1), pp. 21–44. [CrossRef]
Dragan, A. , Lee, K. , and Srinivasa, S. , 2013, “ Legibility and Predictability of Robot Motion,” Eighth ACM/IEEE International Conference on Human-Robot Interaction (HRI), Tokyo, Japan, Mar. 3–6, pp. 301–308.





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