EMG-Controlled Non-Anthropomorphic Hand Teleoperation Using a Continuous Teleoperation Subspace

Meeker, Cassie; Ciocarlie, Matei

doi:10.1109/icra.2019.8794108

Cited by 8 publications

(3 citation statements)

References 26 publications

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“…Our experiments in this paper show that the subspace is relevant for at least three different hands, and we would like to continue to show that is relevant for other hands with different kinematic configurations. We have already shown that the teleoperation subspace is suitable for lower dimensional controls, like electromyography (EMG) [37] and would like to validate this control with more kinematic configurations as well. Finally, we would like to show that the subspace is useful for more complex tasks, like assembling and disassembling machinery.…”

Section: Discussionmentioning

confidence: 99%

A Continuous Teleoperation Subspace with Empirical and Algorithmic Mapping Algorithms for Non-Anthropomorphic Hands

Meeker¹,

Haas-Heger²,

Ciocarlie³

2019

Preprint

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Teleoperation is a valuable tool for robotic manipulators in highly unstructured environments. However, finding an intuitive mapping between a human hand and a nonanthropomorphic robot hand can be difficult, due to the hands' dissimilar kinematics. In this paper, we seek to create a mapping between the human hand and a fully actuated, nonanthropomorphic robot hand that is intuitive enough to enable effective real-time teleoperation, even for novice users. To accomplish this, we propose a low-dimensional teleoperation subspace which can be used as an intermediary for mapping between hand pose spaces. We present two different methods to define the teleoperation subspace: an empirical definition, which requires a person to define hand motions in an intuitive, hand-specific way, and an algorithmic definition, which is kinematically independent, and uses objects to define the subspace. We use each of these definitions to create a teleoperation mapping for different hands. We validate both the empirical and algorithmic mappings with teleoperation experiments controlled by novices and performed on two kinematically distinct hands. The experiments show that the proposed subspace is relevant to teleoperation, intuitive enough to enable control by novices, and can generalize to nonanthropomorphic hands with different kinematic configurations.

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Section: Discussionmentioning

confidence: 99%

A Continuous Teleoperation Subspace with Empirical and Algorithmic Mapping Algorithms for Non-Anthropomorphic Hands

Meeker¹,

Haas-Heger²,

Ciocarlie³

2019

Preprint

Self Cite

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show abstract

“…Evaluation of control scheme effectiveness is typically done by comparing user performance across a set of experimenter-designed control schemes on a predefined control interface ( Rosa et al, 2015 ; El-Hussieny et al, 2018 ; Wang et al, 2018 ; Meeker and Ciocarlie, 2019 ). An alternative, more user-centered approach to control scheme design is to base the control scheme off of information acquired from users in the form of survey or motion data ( Li et al, 2020 ; Losey et al, 2020 ; Bobu et al, 2023 ).…”

Section: Related Workmentioning

confidence: 99%

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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“…Since its first appearance in the '40s (Leon Gillis, 1948), myoelectric control has established itself as an effective mean of controlling artificial limbs. Nowadays, its range of application extends to all those fields of robotics in which humans have to control robots, as teleoperation (Vogel et al, 2011; Meeker and Ciocarlie, 2018), assistive robotics (Song et al, 2008), supernumerary limbs (Hussain et al, 2016; Leigh and Maes, 2016; Ciullo et al, 2018), and of course prosthetics (Segil et al, 2014; Godfrey et al, 2018; i limb, 2018; Michelangelo, 2018; Taska, 2018; Vincent, 2018).…”

Section: Introductionmentioning

confidence: 99%

Design and Assessment of Control Maps for Multi-Channel sEMG-Driven Prostheses and Supernumerary Limbs

et al. 2019

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Proportional and simultaneous control algorithms are considered as one of the most effective ways of mapping electromyographic signals to an artificial device. However, the applicability of these methods is limited by the high number of electromyographic features that they require to operate—typically twice as many the actuators to be controlled. Indeed, extracting many independent electromyographic signals is challenging for a number of reasons—ranging from technological to anatomical. On the contrary, the number of actively moving parts in classic prostheses or extra-limbs is often high. This paper faces this issue, by proposing and experimentally assessing a set of algorithms which are capable of proportionally and simultaneously control as many actuators as there are independent electromyographic signals available. Two sets of solutions are considered. The first uses as input electromyographic signals only, while the second adds postural measurements to the sources of information. At first, all the proposed algorithms are experimentally tested in terms of precision, efficiency, and usability on twelve able-bodied subjects, in a virtual environment. A state-of-the-art controller using twice the amount of electromyographic signals as input is adopted as benchmark. We then performed qualitative tests, where the maps are used to control a prototype of upper limb prosthesis. The device is composed of a robotic hand and a wrist implementing active prono-supination movement. Eight able-bodied subjects participated to this second round of testings. Finally, the proposed strategies were tested in exploratory experiments involving two subjects with limb loss. Results coming from the evaluations in virtual and realistic settings show encouraging results and suggest the effectiveness of the proposed approach.

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EMG-Controlled Non-Anthropomorphic Hand Teleoperation Using a Continuous Teleoperation Subspace

Cited by 8 publications

References 26 publications

A Continuous Teleoperation Subspace with Empirical and Algorithmic Mapping Algorithms for Non-Anthropomorphic Hands

A Continuous Teleoperation Subspace with Empirical and Algorithmic Mapping Algorithms for Non-Anthropomorphic Hands

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

Design and Assessment of Control Maps for Multi-Channel sEMG-Driven Prostheses and Supernumerary Limbs

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