Rebellion and Obedience: The Effects of Intention Prediction in Cooperative Handheld Robots

Stolzenwald, Janis; Mayol-Cuevas, Walterio

doi:10.1109/iros40897.2019.8967927

Cited by 10 publications

(9 citation statements)

References 18 publications

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“…If the robot could not arrive at that target in time, they remapped the virtual element to a physical point within the EHD's reachable space. Stolzenwald et al [15] introduced a model that predicts users' interaction location targets based on their eye gaze and task states using a hand-held robot. This model derives intention from the combined information about the user's gaze pattern and task knowledge.…”

Section: User Motion Predictionmentioning

confidence: 99%

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Improving Haptic Response for Contextual Human Robot Interaction

Mugisha

Guda

Chevallereau

et al. 2022

Sensors

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For haptic interaction, a user in a virtual environment needs to interact with proxies attached to a robot. The device must be at the exact location defined in the virtual environment in time. However, due to device limitations, delays are always unavoidable. One of the solutions to improve the device response is to infer human intended motion and move the robot at the earliest time possible to the desired goal. This paper presents an experimental study to improve the prediction time and reduce the robot time taken to reach the desired position. We developed motion strategies based on the hand motion and eye-gaze direction to determine the point of user interaction in a virtual environment. To assess the performance of the strategies, we conducted a subject-based experiment using an exergame for reach and grab tasks designed for upper limb rehabilitation training. The experimental results in this study revealed that eye-gaze-based prediction significantly improved the detection time by 37% and the robot time taken to reach the target by 27%. Further analysis provided more insight on the effect of the eye-gaze window and the hand threshold on the device response for the experimental task.

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Section: User Motion Predictionmentioning

confidence: 99%

“…We introduce and compare three strategies to detect human intention using the eye gaze and the hand motion to improve the human immersion.We use the eye-gaze detection rather than the eye-gaze attention used in [2,15,16]; 2.…”

Section: Introductionmentioning

confidence: 99%

Improving Haptic Response for Contextual Human Robot Interaction

Mugisha

Guda

Chevallereau

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…These features contribute to a decreased workers' task load. Further work has introduced a 6-DoF kinematics design [9] and extensive research investigated robot-human communication for user guidance [10] and the perception of users' attention and intention for improved cooperation [11], [12]. These works were motivated by the question how handheld robots and humans benefit from each others' strengths within a single-user collaborative setup.…”

Section: A Handheld Robotsmentioning

confidence: 99%

“…Handheld robots [8]- [12] are intelligent tools that process task knowledge and environment information, Department of Computer Science, University of Bristol, UK, .... janis.stolzenwald.2015@my.bristol.ac.uk, wmayol@cs.bris.ac.uk which allows for semi-autonomous assistance in collaborative task solving, and combine these with the natural competences of human users for negotiating obstacles and resolving complex motion planning tasks effortlessly. We argue, that such a system could bridge the aforementioned gap between remote guidance and telemanipulation, with the handheld robot helping both the effective communication between the workers and task outcomes.…”

Section: Introductionmentioning

confidence: 99%

Reach Out and Help: Assisted Remote Collaboration through a Handheld Robot

Stolzenwald,

Mayol-Cuevas

2019

Preprint

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We explore a remote collaboration setup, which involves three parties: a local worker, a remote helper and a handheld robot carried by the local worker. We propose a system that allows a remote user to assist the local user through diagnosis, guidance and physical interaction as a novel aspect with the handheld robot providing task knowledge and enhanced motion and accuracy capabilities. Through experimental studies, we assess the proposed system in two different configurations: with and without the robot's assistance in terms of object interactions and task knowledge. We show that the handheld robot can mediate the helper's instructions and remote object interactions while the robot's semi-autonomous features improve task performance by 24%, reduce the workload for the remote user and decrease required communication bandwidth between both users. This study is a first attempt to evaluate how this new type of collaborative robot works in a remote assistance scenario, a setup that we believe is important to leverage current robot constraints and existing communication technologies.

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“…Despite the potential advantages of the egocentric-based head and eye gesture recognition, this problem has been scarcely addressed in HRI. Exceptions include a system to request and guide a robot to find some object [29], helping navigation to wheelchair users with limited hand mobility [14], or predicting user intention from gaze in grasping tasks [13] and hand-held robots [24]. Most of these works have in common that they seek to assist disabled people.…”

Section: Introductionmentioning

confidence: 99%

Head and eye egocentric gesture recognition for human-robot interaction using eyewear cameras

Marina-Miranda,

Traver

2022

Preprint

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Non-verbal communication plays a particularly important role in a wide range of scenarios in Human-Robot Interaction (HRI). Accordingly, this work addresses the problem of human gesture recognition. In particular, we focus on head and eye gestures, and adopt an egocentric (first-person) perspective using eyewear cameras. We argue that this egocentric view offers a number of conceptual and technical benefits over sceneor robot-centric perspectives.A motion-based recognition approach is proposed, which operates at two temporal granularities. Locally, frame-to-frame homographies are estimated with a convolutional neural network (CNN). The output of this CNN is input to a long shortterm memory (LSTM) to capture longer-term temporal visual relationships, which are relevant to characterize gestures.Regarding the configuration of the network architecture, one particularly interesting finding is that using the output of an internal layer of the homography CNN increases the recognition rate with respect to using the homography matrix itself. While this work focuses on action recognition, and no robot or user study has been conducted yet, the system has been designed to meet real-time constraints. The encouraging results suggest that the proposed egocentric perspective is viable, and this proof-of-concept work provides novel and useful contributions to the exciting area of HRI.

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Rebellion and Obedience: The Effects of Intention Prediction in Cooperative Handheld Robots

Cited by 10 publications

References 18 publications

Improving Haptic Response for Contextual Human Robot Interaction

Improving Haptic Response for Contextual Human Robot Interaction

Reach Out and Help: Assisted Remote Collaboration through a Handheld Robot

Head and eye egocentric gesture recognition for human-robot interaction using eyewear cameras

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