Comparison of Interaction Modalities for Mobile Indoor Robot Guidance: Direct Physical Interaction, Person Following, and Pointing Control

Jevtić, Aleksandar; Doisy, Guillaume; Parmet, Yisrael; Edan, Yael

doi:10.1109/thms.2015.2461683

Cited by 51 publications

(21 citation statements)

References 84 publications

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“…Various pointing recognition methods have been proposed in literature, which were tailored according to system's sensing abilities, e.g., finger tracking [26], or task requirements, e.g., distance of the pointing target [27]. Our previous studies showed that the pointing recognition using the position of the elbow and wrist joints can successfully be applied to robot control in close human-robot interaction [28], [29]. The user-tracking algorithm described in Section II-C3 provides the position of the arm joints in real- …”

Section: Algorithmsmentioning

confidence: 99%

Personalized Robot Assistant for Support in Dressing

Jevtić

Valle

Alenyà

et al. 2019

IEEE Trans. Cogn. Dev. Syst.

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Abstract-Robot-assisted dressing is performed in close physical interaction with users who may have a wide range of physical characteristics and abilities. Design of user adaptive and personalized robots in this context is still indicating limited, or no consideration, of specific user-related issues. This paper describes the development of a multi-modal robotic system for a specific dressing scenario -putting on a shoe, where users' personalized inputs contribute to a much improved task success rate. We have developed: 1) user tracking, gesture recognition and posture recognition algorithms relying on images provided by a depth camera; 2) a shoe recognition algorithm from RGB and depth images; 3) speech recognition and text-to-speech algorithms implemented to allow verbal interaction between the robot and user. The interaction is further enhanced by calibrated recognition of the users' pointing gestures and adjusted robot's shoe delivery position. A series of shoe fitting experiments have been performed on two groups of users, with and without previous robot personalization, to assess how it affects the interaction performance. Our results show that the shoe fitting task with the personalized robot is completed in shorter time, with a smaller number of user commands and reduced workload.

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Section: Algorithmsmentioning

confidence: 99%

Personalized Robot Assistant for Support in Dressing

Jevtić

Valle

Alenyà

et al. 2019

IEEE Trans. Cogn. Dev. Syst.

Self Cite

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“…Numerous methods for gesture recognition have been proposed in literature and they were based on statistical modelling, computer vision, pattern recognition, and so forth [12]. Release of affordable RGB-D cameras such as the Microsoft Kinect allowed development of novel algorithms for human body segmentation [13], which improved the performance of the state-of-the-art gesture recognition methods, also for robotics applications [14]. In the current work, a hands-tracking algorithm was applied to robot manipulators teleoperation and was combined with RL to develop a system that is able to learn from user intervention.…”

Section: Relevant Workmentioning

confidence: 99%

Robot motion adaptation through user intervention and reinforcement learning

Jevtić

Colomé

Alenyà

et al. 2018

Pattern Recognition Letters

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Assistant robots are designed to perform specific tasks for the user, but their performance is rarely optimal, hence they are required to adapt to user preferences or new task requirements. In the previous work, the potential of an interactive learning framework based on user intervention and reinforcement learning (RL) was assessed. The framework allowed the user to correct an unfitted segment of the robot trajectory by using hand movements to guide the robot along a corrective path. So far, only the usability of the framework was evaluated through experiments with users. In the current work, the framework is described in detail and its ability to learn from a set of sample trajectories using an RL algorithm is analyzed. To evaluate the learning performance, three versions of the framework are proposed that differ in the method used to obtain the sample trajectories, which are: human-guided learning, autonomous learning, and combined human-guided with autonomous learning. The results show that the combination of the human-guided and autonomous learning achieved the best performance, and although it needed a higher number of sample trajectories than the human-guided learning, it required less user involvement. Autonomous learning alone obtained the lowest reward value and needed the highest number of sample trajectories.

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“…Various methods for gesture recognition from RGB cameras have been proposed in literature [12]. In our previous work, we showed that the recognition of pointing gestures from depth images can be applied to real-time mobile robot guidance [13]. Here, the concept of visual robot guidance is implemented on robot manipulators and a Reinforcement Learning (RL) algorithm is applied to produce an improved robot motion segment from a series of user interventions.…”

Section: Relevant Workmentioning

confidence: 99%

“…Participants completed the raw NASA-TLX questionnaire after each experiment. The questionnaire enables the collection of six dimensions of workload ranging from 0 to 100, and was used to assess the overall participant workload when performing the experiments, similarly as in [13]. The overall workload is computed as the average of the above-mentioned six dimensions.…”

Section: Performance and Workload Measuresmentioning

confidence: 99%

User Evaluation of an Interactive Learning Framework for Single-Arm and Dual-Arm Robots

et al. 2016

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Abstract. Social robots are expected to adapt to their users and, like their human counterparts, learn from the interaction. In our previous work, we proposed an interactive learning framework that enables a user to intervene and modify a segment of the robot arm trajectory. The framework uses gesture teleoperation and reinforcement learning to learn new motions. In the current work, we compared the user experience with the proposed framework implemented on the single-arm and dual-arm Barrett's 7-DOF WAM robots equipped with a Microsoft Kinect camera for user tracking and gesture recognition. User performance and workload were measured in a series of trials with two groups of 6 participants using two robot settings in different order for counterbalancing. The experimental results showed that, for the same task, users required less time and produced shorter robot trajectories with the single-arm robot than with the dual-arm robot. The results also showed that the users who performed the task with the single-arm robot first experienced considerably less workload in performing the task with the dual-arm robot while achieving a higher task success rate in a shorter time.

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Comparison of Interaction Modalities for Mobile Indoor Robot Guidance: Direct Physical Interaction, Person Following, and Pointing Control

Cited by 51 publications

References 84 publications

Personalized Robot Assistant for Support in Dressing

Personalized Robot Assistant for Support in Dressing

Robot motion adaptation through user intervention and reinforcement learning

User Evaluation of an Interactive Learning Framework for Single-Arm and Dual-Arm Robots

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