1,561 research outputs found

    Haptic-Guided Shared Control Grasping for Collision-Free Manipulation

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    We propose a haptic-guided shared control system that provides an operator with force cues during reach-to-grasp phase of tele-manipulation. The force cues inform the operator of grasping configuration which allows collision-free autonomous post-grasp movements. Previous studies showed haptic guided shared control significantly reduces the complexities of the teleoperation. We propose two architectures of shared control in which the operator is informed about (1) the local gradient of the collision cost, and (2) the grasping configuration suitable for collision-free movements of an aimed pick-and-place task. We demonstrate the efficiency of our proposed shared control systems by a series of experiments with Franka Emika robot. Our experimental results illustrate our shared control systems successfully inform the operator of predicted collisions between the robot and an obstacle in the robot's workspace. We learned that informing the operator of the global information about the grasping configuration associated with minimum collision cost of post-grasp movements results in a reach-to-grasp time much shorter than the case in which the operator is informed about the local-gradient information of the collision cost

    Haptic Bimanual System for Teleoperation of Time-Delayed Tasks

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    Future touch in industry: exploring sociotechnical imaginaries of tactile (tele)robots

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    This paper explores sociotechnical imaginaries for industrial robotics. It is motivated by the prospect of promoting human-centred industrial futures. Investigating the tactility of labour through a critical social perspective the research attends to the future of tactile (tele)robots and elaborates on the concepts of pedagogic, collaborative and superhuman touch. These concepts are offered as starting points to foster productive dialogues between social scientists, roboticists, environmentalists, policy makers, industrial leaders and labourers (e.g. union representatives). This paper is framed through literature and ethnographic fieldwork that contextualises and maps the dominant sociotechnical imaginaries for a future touch in industry, identifying the role of a comparative-competitive frame in sustaining a splintering of the imaginary towards utopic and dystopic extremes. Against this, the paper draws on interviews with leading roboticists to chart alternative futures where humans and robots may work together as collaborators, not competitors

    Haptic Bimanual System for Teleoperation of Time-Delayed Tasks

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    This paper presents a novel teleoperation system, which has been designed to address challenges in the remote control of spaceborne bimanual robotic tasks. The primary interest for designing this system is to assess and increase the efficacy of users performing bimanual tasks, while ensuring the safety of the system and minimising the user's mental load. This system consists of two seven-axis robots that are remotely controlled through two haptic control interfaces. The mental load of the user is monitored using a head-mounted interface, which collects eye gaze data and provides components for the holographic user interface. The development of this system enables the safe execution of tasks remotely, which is a critical building block for developing and deploying future space missions as well as other high-risk tasks

    The classification and new trends of shared control strategies in telerobotic systems: A survey

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    Shared control, which permits a human operator and an autonomous controller to share the control of a telerobotic system, can reduce the operator's workload and/or improve performances during the execution of tasks. Due to the great benefits of combining the human intelligence with the higher power/precision abilities of robots, the shared control architecture occupies a wide spectrum among telerobotic systems. Although various shared control strategies have been proposed, a systematic overview to tease out the relation among different strategies is still absent. This survey, therefore, aims to provide a big picture for existing shared control strategies. To achieve this, we propose a categorization method and classify the shared control strategies into 3 categories: Semi-Autonomous control (SAC), State-Guidance Shared Control (SGSC), and State-Fusion Shared Control (SFSC), according to the different sharing ways between human operators and autonomous controllers. The typical scenarios in using each category are listed and the advantages/disadvantages and open issues of each category are discussed. Then, based on the overview of the existing strategies, new trends in shared control strategies, including the “autonomy from learning” and the “autonomy-levels adaptation,” are summarized and discussed

    Towards an Architecture for Semiautonomous Robot Telecontrol Systems.

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    The design and development of a computational system to support robot–operator collaboration is a challenging task, not only because of the overall system complexity, but furthermore because of the involvement of different technical and scientific disciplines, namely, Software Engineering, Psychology and Artificial Intelligence, among others. In our opinion the approach generally used to face this type of project is based on system architectures inherited from the development of autonomous robots and therefore fails to incorporate explicitly the role of the operator, i.e. these architectures lack a view that help the operator to see him/herself as an integral part of the system. The goal of this paper is to provide a human-centered paradigm that makes it possible to create this kind of view of the system architecture. This architectural description includes the definition of the role of operator and autonomous behaviour of the robot, it identifies the shared knowledge, and it helps the operator to see the robot as an intentional being as himself/herself

    A learning-based shared control architecture for interactive task execution

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    Shared control is a key technology for various robotic applications in which a robotic system and a human operator are meant to collaborate efficiently. In order to achieve efficient task execution in shared control, it is essential to predict the desired behavior for a given situation or context to simplify the control task for the human operator. To do this prediction, we use Learning from Demonstration (LfD), which is a popular approach for transferring human skills to robots. We encode the demonstrated behavior as trajectory distributions and generalize the learned distributions to new situations. The goal of this paper is to present a shared control framework that uses learned expert distributions to gain more autonomy. Our approach controls the balance between the controller’s autonomy and the human preference based on the distributions of the demonstrated trajectories. Moreover, the learned distributions are autonomously refined from collaborative task executions, resulting in a master-slave system with increasing autonomy that requires less user input with an increasing number of task executions. We experimentally validated that our shared control approach enables efficient task executions. Moreover, the conducted experiments demonstrated that the developed system improves its performances through interactive task executions with our shared control
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