This paper presents a novel lightweight and simple TSA-based (twisted string actuation) wearable haptic glove (ExoTen-Glove). This system is using two independent twisted string actuators with integrated force sensors and small-size DC motors. The proposed system can provide users force feedback during the execution of grasping virtual objects. The design of the TSA-based ExoTen-Glove, description of the TSA system, the controller and the preliminary experimental evaluation of the proposed system has been presented in this paper. This device has been evaluated by an experiment in virtual reality environment using HTC VIVE headset with 2 degrees of freedom grasping tasks, where the participants were squeezing a real spring with their thumb and index finger and compare it with a virtual spring stiffness. The results prove the applicability of the ExoTen-Glove for rehabilitation and haptic purposes.

Smart robotics will be a core feature while migrating from Industry 3.0 (i.e., mass manufacturing) to Industry 4.0 (i.e., customized or social manufacturing). A key characteristic of a smart system is its ability to learn. For smart manufacturing, this means incorporating learning capabilities into the current fixed, repetitive, task-oriented industrial manipulators, thus rendering them 'smart'. In this paper we introduce two reinforcement learning (RL) based compensation methods. The learned correction signal, which compensates for unmodeled aberrations, is added to the existing nominal input with an objective to enhance the control performance. The proposed learning algorithms are evaluated on a 6-DoF industrial robotic manipulator arm to follow different kinds of reference paths, such as square or a circular path, or to track a trajectory on a three dimensional surface. In an extensive experimental study we compare the performance of our learning-based methods with well-known tracking controllers, namely, proportional-derivative (PD), model predictive control (MPC), and iterative learning control (ILC). The experimental results show a considerable performance improvement thanks to our RL-based methods when compared to PD, MPC, and ILC.

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A system architecture is presented to generate sensor-controlled robot tasks from knowledge encoded in a CAD model. This architecture consists of an application layer where the user annotates assembly tasks in the CAD software. A process layer infers the specific robot skills and parameters from the CAD model and annotated data. A control layer executes the complex, force-controlled tasks. A proof-of-concept implementation is made, consisting of an application layer implemented in FreeCAD and a process layer that focuses on using fuzzy inference to generate appropriate skill-dependent process parameters from the geometric CAD information and annotations in the CAD model. In the control layer, a constraintbased control framework is used to robustly execute the assembly tasks. The system is validated on a challenging assembly task involving the assembly of screw compressor parts.