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      Whole-Body Geometric Retargeting for Humanoid Robots

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          Abstract

          Humanoid robot teleoperation allows humans to integrate their cognitive capabilities with the apparatus to perform tasks that need high strength, manoeuvrability and dexterity. This paper presents a framework for teleoperation of humanoid robots using a novel approach for motion retargeting through inverse kinematics over the robot model. The proposed method enhances scalability for retargeting, i.e., it allows teleoperating different robots by different human users with minimal changes to the proposed system. Our framework enables an intuitive and natural interaction between the human operator and the humanoid robot at the configuration space level. We validate our approach by demonstrating whole-body retargeting with multiple robot models. Furthermore, we present experimental validation through teleoperation experiments using two state-of-the-art whole-body controllers for humanoid robots.

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          Continuum Robots for Medical Applications: A Survey

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            Bilateral teleoperation: An historical survey

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              Toward a Framework for Levels of Robot Autonomy in Human-Robot Interaction

              A critical construct related to human-robot interaction (HRI) is autonomy, which varies widely across robot platforms. Levels of robot autonomy (LORA), ranging from teleoperation to fully autonomous systems, influence the way in which humans and robots may interact with one another. Thus, there is a need to understand HRI by identifying variables that influence – and are influenced by – robot autonomy. Our overarching goal is to develop a framework for levels of robot autonomy in HRI. To reach this goal, the framework draws links between HRI and human-automation interaction, a field with a long history of studying and understanding human-related variables. The construct of autonomy is reviewed and redefined within the context of HRI. Additionally, the framework proposes a process for determining a robot’s autonomy level, by categorizing autonomy along a 10-point taxonomy. The framework is intended to be treated as guidelines to determine autonomy, categorize the LORA along a qualitative taxonomy, and consider which HRI variables (e.g., acceptance, situation awareness, reliability) may be influenced by the LORA.
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                Author and article information

                Journal
                22 September 2019
                Article
                1909.10080
                347f645c-b2f8-4041-b8b4-4184aa21dd8b

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                History
                Custom metadata
                2019 IEEE-RAS International Conference on Humanoid Robots
                Equal author contribution from Kourosh Darvish and Yeshasvi Tirupachuri
                cs.RO

                Robotics
                Robotics

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