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Abstract-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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We propose novel haptic guidance methods for a dual-arm telerobotic manipulation system, which are able to deal with several different constraints, such as collisions, joint limits, and singularities. We combine the haptic guidance with shared-control algorithms for autonomous orientation control and collision avoidance meant to further simplify the execution of grasping tasks. The stability of the overall system in various control modalities is presented and analyzed via passivity arguments. In addition, a human subject study is carried out to assess the effectiveness and applicability of the proposed control approaches both in simulated and real scenarios. Results show that the proposed haptic-enabled shared-control methods significantly improve the performance of grasping tasks with respect to the use of classic teleoperation with neither haptic guidance nor shared control.

Although robotic telemanipulation has always been a key technology for the nuclear industry, little advancement has been seen over the last decades. Despite complex remote handling requirements, simple mechanically-linked master-slave manipulators still dominate the field. Nonetheless, there is a pressing need for more effective robotic solutions able to significantly speed up the decommissioning of legacy radioactive waste. This paper describes a novel haptic shared-control approach for assisting a human operator in the sort and segregation of different objects in a cluttered and unknown environment. A 3D scan of the scene is used to generate a set of potential grasp candidates on the objects at hand. These grasp candidates are then used to generate guiding haptic cues, which assist the operator in approaching and grasping the objects. The haptic feedback is designed to be smooth and continuous as the user switches from a grasp candidate to the next one, or from one object to another one, avoiding any discontinuity or abrupt changes. To validate our approach, we carried out two human-subject studies, enrolling 15 participants. We registered an average improvement of 20.8%, 20.1%, 32.5% in terms of completion time, linear trajectory, and perceived effectiveness, respectively, between the proposed approach and standard teleoperation.

This paper addresses the problem of mixed initiative, shared control for master-slave grasping and manipulation. We propose a novel system, in which an autonomous agent assists a human in teleoperating a remote slave arm/gripper, using a haptic master device. Our system is designed to exploit the human operator's expertise in selecting stable grasps (still an open research topic in autonomous robotics). Meanwhile, a-priori knowledge of: i) the slave robot kinematics, and ii) the desired post-grasp manipulative trajectory, are fed to an autonomous agent which transmits force cues to the human, to encourage maximally manipulable grasp pose selections. Specifically, the autonomous agent provides force cues to the human, during the reach-to-grasp phase, which encourage the human to select grasp poses which maximise manipulation capability during the post-grasp object manipulation phase. We introduce a task-oriented velocity manipulability cost function (TOV), which is used to identify the maximum kinematic capability of a manipulator during post-grasp motions, and feed this back as force cues to the human during the pre-grasp phase. We show that grasps which minimise TOV result in significantly reduced control effort of the manipulator, compared to other feasible grasps. We demonstrate the effectiveness of our approach by experiments with both real and simulated robots.

Robot-assisted cutting is considered an important task in several fields, such as robotic surgery, nuclear decommissioning, waste management, and manufacturing. Despite the complex dexterity requirements of cutting tasks, very simple mechanically-linked master-slave manipulators still dominate many of the above fields (e.g., nuclear robotics). Moreover, even when more dexterous manipulators are available (e.g., in robot-assisted surgery), the employed systems show little or no autonomy, delegating all control to the experience of the human operator. To ameliorate this situation, we present two haptic shared-control approaches for robotic cutting. They are designed to assist the human operator by enforcing different nonholonomic-like constraints representative of the cutting kinematics. To validate our approach, we carried out a humansubject experiment in a real cutting scenario. We compared our shared-control techniques with each other and with a standard haptic teleoperation scheme. Results show the usefulness of assisted control schemes in complex applications such as cutting. However, they also show a discrepancy between objective and subjective metrics.

Robotic telemanipulators are already widely used in nuclear decommissioning sites for handling radioactive waste. However, currently employed systems are still extremely primitive, making the handling of these materials prohibitively slow and ineffective. As the estimated cost for the decommissioning and clean-up of nuclear sites keeps rising, it is clear that one would need faster and more effective approaches. Towards this goal, in this paper we present the user evaluation of a recently proposed haptic-enabled shared-control architecture for telemanipulation. An autonomous algorithm regulates a subset of the slave manipulator degrees of freedom (DoF) in order to help the human operator in grasping an object of interest. The human operator can then steer the manipulator along the remaining null-space directions with respect to the main task by acting on a grounded haptic interface. The haptic cues provided to the operator are designed in order to inform about the feasibility of the user's commands with respect to possible constraints of the robotic system. In this paper we compared this shared-control architecture against a classical 6-DOF teleoperation approach in a real scenario by running experiments with 10 subjects. The results clearly show that the proposed shared-control approach is a viable and effective solution for improving currently-available teleoperation systems in remote telemanipulation tasks.