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Review Article

A Review of Intent Detection, Arbitration, and Communication Aspects of Shared Control for Physical Human–Robot Interaction

[+] Author and Article Information
Dylan P. Losey

Department of Mechanical Engineering,
Rice University,
Houston, TX 77251
e-mail: dlosey@rice.edu

Craig G. McDonald

Department of Mechanical Engineering,
Rice University,
Houston, TX 77251
e-mail: Craig.G.McDonald@rice.edu

Edoardo Battaglia

Centro di Ricerca “E. Piaggio”,
University of Pisa,
Pisa 56126, Italy
e-mail: e.battaglia@centropiaggio.unipi.it

Marcia K. O'Malley

Professor
Fellow ASME
Mechatronics and Haptic Interfaces Laboratory,
Department of Mechanical Engineering,
Rice University,
Houston, TX 77251
e-mail: omalleym@rice.edu

Manuscript received January 11, 2017; final manuscript received January 22, 2018; published online February 14, 2018. Editor: Harry Dankowicz.

Appl. Mech. Rev 70(1), 010804 (Feb 14, 2018) (19 pages) Paper No: AMR-17-1003; doi: 10.1115/1.4039145 History: Received January 11, 2017; Revised January 22, 2018

As robotic devices are applied to problems beyond traditional manufacturing and industrial settings, we find that interaction between robots and humans, especially physical interaction, has become a fast developing field. Consider the application of robotics in healthcare, where we find telerobotic devices in the operating room facilitating dexterous surgical procedures, exoskeletons in the rehabilitation domain as walking aids and upper-limb movement assist devices, and even robotic limbs that are physically integrated with amputees who seek to restore their independence and mobility. In each of these scenarios, the physical coupling between human and robot, often termed physical human robot interaction (pHRI), facilitates new human performance capabilities and creates an opportunity to explore the sharing of task execution and control between humans and robots. In this review, we provide a unifying view of human and robot sharing task execution in scenarios where collaboration and cooperation between the two entities are necessary, and where the physical coupling of human and robot is a vital aspect. We define three key themes that emerge in these shared control scenarios, namely, intent detection, arbitration, and feedback. First, we explore methods for how the coupled pHRI system can detect what the human is trying to do, and how the physical coupling itself can be leveraged to detect intent. Second, once the human intent is known, we explore techniques for sharing and modulating control of the coupled system between robot and human operator. Finally, we survey methods for informing the human operator of the state of the coupled system, or the characteristics of the environment with which the pHRI system is interacting. At the conclusion of the survey, we present two case studies that exemplify shared control in pHRI systems, and specifically highlight the approaches used for the three key themes of intent detection, arbitration, and feedback for applications of upper limb robotic rehabilitation and haptic feedback from a robotic prosthesis for the upper limb.

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Figures

Grahic Jump Location
Fig. 1

Conceptual representation of the proposed framework: human and robot exchange information and interact with the environment according to what is decided by the arbitration (represented by the knob)

Grahic Jump Location
Fig. 2

The three steps for conveying of the human's intent to the robot: identification, measurement, and interpretation

Grahic Jump Location
Fig. 3

A simple arbitration between human and robot, where together the human and robot are sharing control of the position of the robot's end effector. On the left panel, the robot uses intent detection in order to infer the human's desired motion, uh. In the middle panel, we show the robot's intended direction of motion, ur, which is tangent to the desired trajectory. Finally, on the right panel, we arbitrate between the human and robot intents, and thus the robot's end effector moves in a direction u, which compromises between uh and ur.

Grahic Jump Location
Fig. 4

Experimental setup used by Reed and Peshkin [38]. Two human partners are working together to rotate a crank to a desired orientation (gray boxes). During these experiments, the vision of the partners was occluded, and only haptic communication was allowed. It was found that human partners naturally assume different roles, and that performance improves when the task is performed by human dyads, as opposed to a single human operator.

Grahic Jump Location
Fig. 5

Using a virtual tunnel to arbitrate roles between human and robot, the human and robot are attempting to follow a desired trajectory during a 1DoF task. The current position, x, is given by the torus, and the desired position, xd, is given bythe sphere. When the current position is within the virtualtunnel, the robot does not provide the human any assistance. When the current position is outside of the virtual tunnel, like shown, the robot provides haptic feedback (arrows) to guide the human back toward the desired trajectory. This combines both master–slave (human master, robotic slave) andteacher–student (robotic teacher, human student) role arbitrations.

Grahic Jump Location
Fig. 6

Simplified schematic of communication between the human and robot during shared control. Three different modalities of communication are shown: haptic, visual, and aural feedback. Feedback is based on the robot's interaction with the environment, on right, where the environment could be virtual (such as in rehabilitation) or physical (such as for prosthetics). Thus, the kinesthetic haptic feedback force, fa, emulates virtualor real robot–environment interaction forces. The visual feedback depicts the desired trajectory, as well as the desired position at the current time, xd. Aural feedback can provide information on errors or instructions to assist the human operator.

Grahic Jump Location
Fig. 7

RiceWrist-S wrist-forearm exoskeleton, with labeled joints for pronation/supination (PS), flexion/extension (FE), and radial/ulnar deviation (RU). The human and robot share control of the handle position during trajectory following tasks, with applications in upper-limb rehabilitation (Reproduced with permission from Pehlivan et al. [28]. Copyright 2016 by IEEE).

Grahic Jump Location
Fig. 8

Comparison of pre-existing RBF intent detection scheme (dark bar) with our proposed KF intent detection approach (light bar). The normalized error between the actual and estimated human intents for both methods is plotted over 20 s intervals (disturbance estimation error). After 60 s, nonposition-dependent human inputs of increasing magnitude were applied (circles) (Reproduced with permission from Pehlivan et al. [28]. Copyright 2016 by IEEE).

Grahic Jump Location
Fig. 9

Average trajectory velocities with our decay algorithm (left) and without our decay algorithm (middle). When the decay algorithm was present, arbitration is dynamically shifted toward able subjects, and users were allowed to reach the goal more quickly than the reference trajectory (right) (Reproduced with permission from Pehlivan et al. [28]. Copyright 2016 by IEEE).

Grahic Jump Location
Fig. 10

First implementation of the Pisa/IIT SoftHand (left—Reproduced with permission from Catalano et al. [157]. Copyright 2014 by Sage Ltd.) and a recent release of the SoftHand Pro (right—Reproduced with permission from Fani et al. [162]. Copyright 2016 by authors, including Antonio Bicchi and Marco Santello.)

Grahic Jump Location
Fig. 11

Clenching upper-limb force feedback haptic device (Reproduced with permission from Casini et al. [164]. Copyright 2015 by IEEE.)

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