Can Teams Outperform Individuals in a Simulated Dynamic Control Task?

Glynn, Steven J.; Henning, Robert A.

doi:10.1177/154193120004403316

Cited by 18 publications

(17 citation statements)

References 8 publications

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“…The subjects had to actively track the target to perform the task. Our experimental paradigm thus differed from previous studies14151617181920 in two aspects. First, unlike most previous studies which ask subjects to perform a single task together, subjects in this paradigm performs their own individual tasks.…”

Section: Resultsmentioning

confidence: 92%

Two is better than one: Physical interactions improve motor performance in humans

Ganesh

Takagi

Osu³

et al. 2014

Sci Rep

187

260

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How do physical interactions with others change our own motor behavior? Utilizing a novel motor learning paradigm in which the hands of two - individuals are physically connected without their conscious awareness, we investigated how the interaction forces from a partner adapt the motor behavior in physically interacting humans. We observed the motor adaptations during physical interactions to be mutually beneficial such that both the worse and better of the interacting partners improve motor performance during and after interactive practice. We show that these benefits cannot be explained by multi-sensory integration by an individual, but require physical interaction with a reactive partner. Furthermore, the benefits are determined by both the interacting partner's performance and similarity of the partner's behavior to one's own. Our results demonstrate the fundamental neural processes underlying human physical interactions and suggest advantages of interactive paradigms for sport-training and physical rehabilitation.

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Section: Resultsmentioning

confidence: 92%

Two is better than one: Physical interactions improve motor performance in humans

Ganesh

Takagi

Osu³

et al. 2014

Sci Rep

187

260

View full text Add to dashboard Cite

show abstract

“…In some FBW airplane designs, the command given to the airplane control surfaces is an electronic average of the position from the two pilots' flight controls [20]. Glynn and Henning [23] conducted an experiment where the motion of a mass was controlled by averaging two participants' force inputs. They found that haptic feedback decreased errors and improved performance when visual and haptic feedbacks were synchronous.…”

Section: Introductionmentioning

confidence: 99%

Physical Collaboration of Human-Human and Human-Robot Teams

Reed

Peshkin

2008

IEEE Trans. Haptics

195

163

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Abstract-Human partners working on a target acquisition task perform faster than do individuals on the same task, even though the partners consider each other to be an impediment. We recorded the force profile of each partner during the task, revealing an emergent specialization of roles that could only have been negotiated through a haptic channel. With this understanding of human haptic communication, we attempted a "Haptic Turing Test," replicating human behaviors in a robot partner. Human participants consciously and incorrectly believed their partner was human. However, force profiles did not show specialization of roles in the human partner, nor enhanced dyadic performance, suggesting that haptic interaction holds a greater subconscious subtlety. We further report observations of a nonzero dyadic steady-state force perhaps analogous to cocontraction within the limb of an individual, where it contributes to limb stiffness and disturbance rejection. We present results on disturbance rejection in a dyad, showing lack of an effective dyadic strategy for brief events.

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“…(Desai and Yanco, 2005) proposed a linear blend in two dimension between maximum user speed and maximum robot speed. Outside the teleoperation domain, a type of arbitration -averaging -is used for mediating between two human input channels (Glynn and Henning, 2000), and blending in general between two human policies is used in surgery training (Nudehi et al, 2005). Therefore, an intuitive formalism -linear policy blending -can act as a common lens across a wide range of literature, enabling us to identify prediction and arbitration as two key challenges that a shared control robot faces.…”

Section: Prior Workmentioning

confidence: 99%

A policy-blending formalism for shared control

Dragan

Srinivasa

2013

The International Journal of Robotics Research

298

312

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Abstract-In shared control teleoperation, the robot assists the user in accomplishing 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 propose an intuitive formalism that captures assistance as policy blending, illustrate how some of the existing techniques for shared control instantiate it, 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. We define the arbitration problem from a control-theoretic perspective, and turn our attention to what users consider good arbitration. We conduct a user study that analyzes the effect of different factors on the performance of assistance, indicating that arbitration should be contextual: it should depend on the robot's confidence in itself and in the user, and even the particulars of the user. Based on the study, we discuss challenges and opportunities that a robot sharing the control with the user might face: adaptation to the context and the user, legibility of behavior, and the closed loop between prediction and user behavior.

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Can Teams Outperform Individuals in a Simulated Dynamic Control Task?

Cited by 18 publications

References 8 publications

Two is better than one: Physical interactions improve motor performance in humans

Two is better than one: Physical interactions improve motor performance in humans

Physical Collaboration of Human-Human and Human-Robot Teams

A policy-blending formalism for shared control

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