Real-Time 6DOF Pose Relocalization for Event Cameras with Stacked Spatial LSTM Networks

We present a new method to relocalize the 6DOF pose of an event camera solely based on the event stream. Our method first creates the event image from a list of events that occurs in a very short time interval, then a Stacked Spatial LSTM Network (SP-LSTM) is used to learn the camera pose. Our SP-LSTM is composed of a CNN to learn deep features from the event images and a stack of LSTM to learn spatial dependencies in the image feature space. We show that the spatial dependency plays an important role in the relocalization task and the SP-LSTM can effectively learn this information. The experimental results on a publicly available dataset show that our approach generalizes well and outperforms recent methods by a substantial margin. Overall, our proposed method reduces by approx. 6 times the position error and 3 times the orientation error compared to the current state of the art. The source code and trained models will be released.Comment: 7 pages, 5 figure

Translating Videos to Commands for Robotic Manipulation with Deep Recurrent Neural Networks

We present a new method to translate videos to commands for robotic manipulation using Deep Recurrent Neural Networks (RNN). Our framework first extracts deep features from the input video frames with a deep Convolutional Neural Networks (CNN). Two RNN layers with an encoder-decoder architecture are then used to encode the visual features and sequentially generate the output words as the command. We demonstrate that the translation accuracy can be improved by allowing a smooth transaction between two RNN layers and using the state-of-the-art feature extractor. The experimental results on our new challenging dataset show that our approach outperforms recent methods by a fair margin. Furthermore, we combine the proposed translation module with the vision and planning system to let a robot perform various manipulation tasks. Finally, we demonstrate the effectiveness of our framework on a full-size humanoid robot WALK-MAN

A multi-DOF robotic exoskeleton interface for hand motion assistance

This paper outlines the design and development of a robotic exoskeleton based rehabilitation system. A portable direct-driven optimized hand exoskeleton system has been proposed. The optimization procedure primarily based on matching the exoskeleton and finger workspaces guided the system design. The selection of actuators for the proposed system has emerged as a result of experiments with users of different hand sizes. Using commercial sensors, various hand parameters, e.g. maximum and average force levels have been measured. The results of these experiments have been mapped directly to the mechanical design of the system. An under-actuated optimum mechanism has been analysed followed by the design and realization of the first prototype. The system provides both position and force feedback sensory information which can improve the outcomes of a professional rehabilitation exercise. © 2011 IEEE

A reduced-complexity description of arm endpoint stiffness with applications to teleimpedance control

Effective and stable execution of a remote manipulation task in an uncertain environment requires that the task force and position trajectories of the slave robot be appropriately commanded. To achieve this goal, in teleimpedance control, a reference command which consists of the stiffness and position profiles of the master is computed and realized by the compliant slave robot in real-time. This highlights the need for a suitable and computationally efficient tracking of the human limb stiffness profile in real-time. In this direction, based on the observations in human neuromotor control which give evidence on the predominant use of the arm configuration in directional adjustments of the endpoint stiffness profile, and the role of muscular co-activations which contribute to a coordinated regulation of the task stiffness in all directions, we propose a novel and computationally efficient model of the arm endpoint stiffness behaviour. Real-time tracking of the human arm kinematics is achieved using an arm triangle monitored by three markers placed at the shoulder, elbow and wrist level. In addition, a co-contraction index is defined using muscular activities of a dominant antagonistic muscle pair. Calibration and identification of the model parameters are carried out experimentally, using perturbation-based arm endpoint stiffness measurements in different arm configurations and co-contraction levels of the chosen muscles. Results of this study suggest that the proposed model enables the master to naturally execute a remote task by modulating the direction of the major axes of the endpoint stiffness and its volume using arm configuration and the co-activation of the involved muscles, respectively

Design of a 2-Finger Hand Exoskeleton for Finger Stiffness Measurements

Recent studies of human arm movements have suggested that the control of stiffness may be important both for maintaining stability and for achieving differences in movement accuracy. Several studies in the robotic field demonstrated that grasp stiffness is useful for modelling and controlling manipulators but, even though it is accredited that having models of the human finger impedance would be very desirable for the control of anthropomorphous robot's hands, relatively few studies have focused on finger and hand stiffness. To allow the measurement of such entities at the finger level, an appropriate device capable of applying fast force transients while at the same time be able to monitor the finger movements is required. The work presented in this paper is a very detailed report about the design of a new hand exoskeleton system that will be used in our future works to investigate the finger stiffness range in different grasping postures and conditions