How Autonomy Impacts Performance and Satisfaction: Results From a Study With Spinal Cord Injured Subjects Using an Assistive Robot

Kim, Dae‐Jin; Hazlett-Knudsen, Rebekah; Culver-Godfrey, H.; Rucks, Greta; Cunningham, Tommy J.; Portee, D.; Bricout, John C.; Wang, Zhao; Behal, Aman

doi:10.1109/tsmca.2011.2159589

Cited by 130 publications

(130 citation statements)

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“…Instances of arbitration also appear outside the teleoperation domain, when mediating between two human input channels [25]. Analyzing assistance based on how arbitration is done, together new factors like prediction correctness and task difficulty, helps explain previously contradictory findings: our results show that aggressive assistance is preferable on hard tasks, like the ones from [4], where autonomy is significantly more efficient; opinions are split on easier tasks, like the ones from [5], where the autonomous and manual mode were comparable in terms of time to completion.…”

Section: Methodsmentioning

confidence: 67%

“…For example, You and Hauser [4] found that for a complex motion planning problem in a simulated environment, users preferred a fully autonomous mode, where they only clicked on the desired goal, to more reactive modes of assistance. On the other hand, Kim et al [5] found that users preferred a manual mode and not the autonomous one for manipulation tasks like object grasping.…”

Section: Prior Workmentioning

confidence: 99%

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Formalizing Assistive Teleoperation

Dragan

Srinivasa

2012

Robotics: Science and Systems VIII

107

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Abstract-In assistive teleoperation, the robot helps the user accomplish the desired task, making teleoperation easier and more seamless. Rather than simply executing the user's input, which is hindered by the inadequacies of the interface, the robot attempts to predict the user's intent, and assists in accomplishing it. In this work, we are interested in the scientific underpinnings of assistance: we formalize assistance under the general framework of policy blending, show how previous work methods instantiate this formalism, and provide a principled analysis of its main components: prediction of user intent and its arbitration with the user input. We define the prediction problem, with foundations in Inverse Reinforcement Learning, discuss simplifying assumptions that make it tractable, and test these on data from users teleoperating a robotic manipulator under various circumstances. We propose that arbitration should be moderated by the confidence in the prediction. Our user study analyzes the effect of the arbitration type, together with the prediction correctness and the task difficulty, on the performance of assistance and the preferences of users.

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Section: Methodsmentioning

confidence: 67%

Section: Prior Workmentioning

confidence: 99%

Formalizing Assistive Teleoperation

Dragan

Srinivasa

2012

Robotics: Science and Systems VIII

107

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“…The history of assistive intelligence for teleoperation begins in 1963, with the work of Goertz (Goertz, 1963) on using teleoperated manipulators for handling radioactive material. Since then, research on this topic has proposed a great variety of methods for assistance, ranging from the robot having full control over all or some aspect of the motion (Rosenberg, 1993;Marayong, Li, Okamura, & Hager, 2003;Debus, Stoll, Howe, & Dupont, 2000;You & Hauser, 2011;Hauser, 2012;D.-J. Kim et al, 2011;Marayong, Okamura, & Bettini, 2002;Demiris & Hayes, 2002;Fagg, Rosenstein, Platt, & Grupen, 2004), to taking control (or releasing it) at some trigger (Kofman, W., Luu, & Verma, 2005;Shen, Ibanez-Guzman, Ng, & Chew, 2004;Anderson, Peters, Iagnemma, & Overholt, 2010), to never fully taking control (Crandall & Goodrich, 2002;Aigner & McCarragher, 1997;You & Hauser, 2011;Marayong et al, 2002;Aarno, Ekvall, & Kragic, 2005).…”

Section: Related Workmentioning

confidence: 99%

“…However, a study by (D.-J. Kim et al, 2011) showed that users preferred a manual teleoperation mode despite a higher cognitive load. We believe that rather than studying autonomy level or aggressiveness on their own, we must study the interaction among aggressiveness and other factors, like task difficulty (users might like assistance more on harder tasks, when they need it more) and prediction correctness (users might dislike assistance when the robot incorrectly predicts their intent).…”

Section: Related Workmentioning

confidence: 99%

“…Kim et al, 2011) and those supporting aggressive assistance (Marayong et al, 2002;You & Hauser, 2011). The explanation is that arbitration must be contextual: it must depend on the robot's confidence in itself (in its prediction, in how well it can achieve the task the user wants to achieve), as well as on the robot's confidence in the user (in how good the user is at achieving the task).…”

Section: Main Lessonmentioning

confidence: 99%

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Teleoperation with Intelligent and Customizable Interfaces

Dragan

Srinivasa

Lee

2013

Journal of Human-Robot Interaction

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In this paper, we explore a class of teleoperation problems where a user controls a sophisticated device (e.g. a robot) via an interface to perform a complex task. Teleoperation interfaces are fundamentally limited by the indirectness of the process, by the fact that the user is not physically executing the task. In this work, we study intelligent and customizable interfaces: these are interfaces that mediate the consequences of indirectness and make teleoperation more seamless. They are intelligent in that they take advantage of the robot's autonomous capabilities and assist in accomplishing the task. They are customizable in that they enable the users to adapt the retargetting function which maps their input onto the robot. Our studies support the advantages of such interfaces, but also point out the challenges they bring. We make three key observations. First, although assistance can greatly improve teleperation, the decision on how to provide assistance must be contextual. It must depend, for example, on the robot's confidence in its prediction of the user's intent. Second, although users do have the ability to provide intent-expressive input that simplifies the robot's prediction task, this ability can be hindered by kinematic differences between themselves and the robot. And third, although interface customization is important, it must be robust to poor examples from the user.

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