Large Scale Capacitive Skin for Robots

Communications engineering / telecommunication

Low-cost Sensor Glove with Force Feedback for Learning from Demonstrations using Probabilistic Trajectory Representations

Sensor gloves are popular input devices for a large variety of applications including health monitoring, control of music instruments, learning sign language, dexterous computer interfaces, and tele-operating robot hands. Many commercial products as well as low-cost open source projects have been developed. We discuss here how low-cost (approx. 250 EUROs) sensor gloves with force feedback can be build, provide an open source software interface for Matlab and present first results in learning object manipulation skills through imitation learning on the humanoid robot iCub.Comment: 3 pages, 3 figures. Workshop paper of the International Conference on Robotics and Automation (ICRA 2015

Chapter Large Scale Capacitive Skin for Robots

Communications engineering / telecommunication

Multisensory wearable interface for immersion and telepresence in robotics

The idea of being present in a remote location has inspired researchers to develop robotic devices that make humans to experience the feeling of telepresence. These devices need of multiple sensory feedback to provide a more realistic telepresence experience. In this work, we develop a wearable interface for immersion and telepresence that provides to human with the capability of both to receive multisensory feedback from vision, touch and audio and to remotely control a robot platform. Multimodal feedback from a remote environment is based on the integration of sensor technologies coupled to the sensory system of the robot platform. Remote control of the robot is achieved by a modularised architecture, which allows to visually explore the remote environment. We validated our work with multiple experiments where participants, located at different venues, were able to successfully control the robot platform while visually exploring, touching and listening a remote environment. In our experiments we used two different robotic platforms: the iCub humanoid robot and the Pioneer LX mobile robot. These experiments show that our wearable interface is comfortable, easy to use and adaptable to different robotic platforms. Furthermore, we observed that our approach allows humans to experience a vivid feeling of being present in a remote environment

感度調整可能な3軸マルチモーダルスキンセンサーモジュールの開発

早大学位記番号:新8538早稲田大

Edge and plane classification with a biomimetic iCub fingertip sensor

The exploration and interaction of humanoid robots with the environment through tactile sensing is an important task for achieving truly autonomous agents. Recently much research has been focused on the development of new technologies for tactile sensors and new methods for tactile exploration. Edge detection is one of the tasks required in robots and humanoids to explore and recognise objects. In this work we propose a method for edge and plane classification with a biomimetic iCub fingertip using a probabilistic approach. The iCub fingertip mounted on an xy-table robot is able to tap and collect the data from the surface and edge of a plastic wall. Using a maximum likelihood classifier the xy-table knows when the iCub fingertip has reached the edge of the object. The study presented here is also biologically inspired by the tactile exploration performed in animals

Design and development of robust hands for humanoid robots

Design and development of robust hands for humanoid robot

Controlled Tactile Exploration and Haptic Object Recognition

In this paper we propose a novel method for in-hand object recognition. The method is composed of a grasp stabilization controller and two exploratory behaviours to capture the shape and the softness of an object. Grasp stabilization plays an important role in recognizing objects. First, it prevents the object from slipping and facilitates the exploration of the object. Second, reaching a stable and repeatable position adds robustness to the learning algorithm and increases invariance with respect to the way in which the robot grasps the object. The stable poses are estimated using a Gaussian mixture model (GMM). We present experimental results showing that using our method the classifier can successfully distinguish 30 objects.We also compare our method with a benchmark experiment, in which the grasp stabilization is disabled. We show, with statistical significance, that our method outperforms the benchmark method

Active contour following to explore object shape with robot touch

In this work, we present an active tactile perception approach for contour following based on a probabilistic framework. Tactile data were collected using a biomimetic fingertip sensor. We propose a control architecture that implements a perception-action cycle for the exploratory procedure, which allows the fingertip to react to tactile contact whilst regulating the applied contact force. In addition' the fingertip is actively repositioned to an optimal position to ensure accurate perception. The method is trained off-line and then the testing performed on-line based on contour following around several different test shapes. We then implement object recognition based on the extracted shapes. Our active approach is compared with a passive approach, demonstrating that active perception is necessary for successful contour following and hence shape recognition