Toward Handling the Complexities of Non-Anthropomorphic Hands

Molnar, Jennifer L.; Mengüç, Yiğit

doi:10.1145/3491101.3519871

Cited by 2 publications

(3 citation statements)

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“…To teleoperate a robot with a non-human morphology, a mapping must be designed to translate the operator’s inputs into the robot’s corresponding actions ( Molnar and Mengüç, 2022 ). This mapping, called a “control scheme,” is constrained by the type of inputs that can be captured by the hardware that serves as the control interface.…”

Section: Introductionmentioning

confidence: 99%

“…The control scheme design challenge was reaffirmed as unsolved as recently as 2022 ( Rea and Seo, 2022 ), in which paper the authors suggest that improvements from user-centered design have trailed improvements in technology, creating a situation in which the main factor limiting teleoperated robot usage is the difficulty of learning to effectively control them. A common approach to control scheme design is for a researcher to design several mappings and then compare user performance for each control scheme ( Wang et al, 2018 ; Hetrick et al, 2020 ; Whitney et al, 2020 ; Meeker et al, 2022 ; Molnar and Mengüç, 2022 ). The control interface is usually pre-determined, and performance is task-dependent, so the researcher selects the performance evaluation tasks based on the particular use case for the robot ( Molnar and Mengüç, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

“…A common approach to control scheme design is for a researcher to design several mappings and then compare user performance for each control scheme ( Wang et al, 2018 ; Hetrick et al, 2020 ; Whitney et al, 2020 ; Meeker et al, 2022 ; Molnar and Mengüç, 2022 ). The control interface is usually pre-determined, and performance is task-dependent, so the researcher selects the performance evaluation tasks based on the particular use case for the robot ( Molnar and Mengüç, 2022 ). In some studies with a more user-centered approach, the user may be asked to provide specific demonstrations using a provided control interface to indicate the control scheme they prefer ( Li et al, 2020 ; Losey et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

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Clustering user preferences for personalized teleoperation control schemes via trajectory similarity analysis

Molnar,

Agrawal,

Chernova

2024

Front. Robot. AI

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Successful operation of a teleoperated robot depends on a well-designed control scheme to translate human motion into robot motion; however, a single control scheme may not be suitable for all users. On the other hand, individual personalization of control schemes may be infeasible for designers to produce. In this paper, we present a method by which users may be classified into groups with mutually compatible control scheme preferences. Users are asked to demonstrate freehand motions to control a simulated robot in a virtual reality environment. Hand pose data is captured and compared with other users using SLAM trajectory similarity analysis techniques. The resulting pairwise trajectory error metrics are used to cluster participants based on their control motions, without foreknowledge of the number or types of control scheme preferences that may exist. The clusters identified for two different robots shows that a small number of clusters form stably for each case, each with its own control scheme paradigm. Survey data from participants validates that the clusters identified through this method correspond to the participants’ control scheme rationales, and also identify nuances in participant control scheme descriptions that may not be obvious to designers relying only on participant explanations of their preferences.

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Section: Introductionmentioning

confidence: 99%