Visualization of Intended Assistance for Acceptance of Shared Control

Brooks, Connor; Szafir, Daniel

doi:10.1109/iros45743.2020.9340964

Cited by 24 publications

(19 citation statements)

References 27 publications

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“…Following an extensive body of literature that uses Hidden Markov Models (HMMs) for human intention estimation ( Tanwani and Calinon, 2017 ; Jain and Argall, 2019 ; Brooks and Szafir, 2020 ), we also develop an HMM to infer target bolts from manipulator data. First, we consider a sequence of observations o 1: t = ( o 1 , … , o t ) as the Euclidean distances from the current end effector’s position

to all other goals:

where the frame n subscript is removed for notational simplicity and

are the three position variables of the end effector at the i th bolt.…”

Section: Shared Control Methodologymentioning

confidence: 99%

“…Recent teleoperation frameworks have addressed model mismatch through immersive interface design ( Zolotas and Demiris, 2019 ; Brooks and Szafir, 2020 ), however the literature is sparse in studies that investigate the role of lifelong assistance ( Demiris, 2009 ). In our pilot survey, numerous participants were appealed by the idea of directly visualizing polytopes, yet struggled to build an intuition for them in the short span of a quick trial.…”

Section: Future Workmentioning

confidence: 99%

“…Following an extensive body of literature that uses Hidden Markov Models (HMMs) for human intention estimation (Tanwani and Calinon, 2017;Jain and Argall, 2019;Brooks and Szafir, 2020), we also develop an HMM to infer target bolts from manipulator data. First, we consider a sequence of observations o 1:t (o 1 , .…”

Section: Human Intention Estimationmentioning

confidence: 99%

“…As a result, we opt to use two prevalent questionnaires within the field of human-robot interaction: the NASA-TLX (Hart and Staveland, 1988) and the System Usability Scale (SUS) created by Brooke (1996). Both rating scales have been applied in similar mixed reality studies (Brooks and Szafir, 2020;Rosen et al, 2020), with the NASA-TLX denoting perceived workload and the SUS relating to usability. Participants completed these questionnaires immediately after every trial.…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…Alternatively, mixed reality headsets are an emerging technology that have recently gained traction in addressing misalignment in shared control ( Zolotas and Demiris, 2019 ; Brooks and Szafir, 2020 ). These headsets are not without flaws, but in the scope of shared control where users must maintain attention and actively participate in the task-at-hand, they hold great promise over other technologies ( Sibirtseva et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

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Motion Polytopes in Virtual Reality for Shared Control in Remote Manipulation Applications

et al. 2021

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In remote applications that mandate human supervision, shared control can prove vital by establishing a harmonious balance between the high-level cognition of a user and the low-level autonomy of a robot. Though in practice, achieving this balance is a challenging endeavor that largely depends on whether the operator effectively interprets the underlying shared control. Inspired by recent works on using immersive technologies to expose the internal shared control, we develop a virtual reality system to visually guide human-in-the-loop manipulation. Our implementation of shared control teleoperation employs end effector manipulability polytopes, which are geometrical constructs that embed joint limit and environmental constraints. These constructs capture a holistic view of the constrained manipulator’s motion and can thus be visually represented as feedback for users on their operable space of movement. To assess the efficacy of our proposed approach, we consider a teleoperation task where users manipulate a screwdriver attached to a robotic arm’s end effector. A pilot study with prospective operators is first conducted to discern which graphical cues and virtual reality setup are most preferable. Feedback from this study informs the final design of our virtual reality system, which is subsequently evaluated in the actual screwdriver teleoperation experiment. Our experimental findings support the utility of using polytopes for shared control teleoperation, but hint at the need for longer-term studies to garner their full benefits as virtual guides.

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to all other goals:

where the frame n subscript is removed for notational simplicity and

are the three position variables of the end effector at the i th bolt.…”

Section: Shared Control Methodologymentioning

confidence: 99%

Section: Future Workmentioning

confidence: 99%

“…Following an extensive body of literature that uses Hidden Markov Models (HMMs) for human intention estimation (Tanwani and Calinon, 2017;Jain and Argall, 2019;Brooks and Szafir, 2020), we also develop an HMM to infer target bolts from manipulator data. First, we consider a sequence of observations o 1:t (o 1 , .…”

Section: Human Intention Estimationmentioning

confidence: 99%