Learning Latent Space Dynamics for Tactile Servoing

To achieve a dexterous robotic manipulation, we need to endow our robot with tactile feedback capability, i.e. the ability to drive action based on tactile sensing. In this paper, we specifically address the challenge of tactile servoing, i.e. given the current tactile sensing and a target/goal tactile sensing --memorized from a successful task execution in the past-- what is the action that will bring the current tactile sensing to move closer towards the target tactile sensing at the next time step. We develop a data-driven approach to acquire a dynamics model for tactile servoing by learning from demonstration. Moreover, our method represents the tactile sensing information as to lie on a surface --or a 2D manifold-- and perform a manifold learning, making it applicable to any tactile skin geometry. We evaluate our method on a contact point tracking task using a robot equipped with a tactile finger. A video demonstrating our approach can be seen in https://youtu.be/0QK0-Vx7WkIComment: Accepted to be published at the International Conference on Robotics and Automation (ICRA) 2019. The final version for publication at ICRA 2019 is 7 pages (i.e. 6 pages of technical content (including text, figures, tables, acknowledgement, etc.) and 1 page of the Bibliography/References), while this arXiv version is 8 pages (added Appendix and some extra details

Magneto-inductive skin sensor for robot collision avoidance: A new development

Safety is a primary concern for robots operating in space. The tri-mode sensor addresses that concern by employing a collision avoidance/management skin around the robot arms. This rf-based skin sensor is at present a dual mode (proximity and tactile). The third mode, pyroelectric, will complement the other two. The proximity mode permits the robot to sense an intruding object, to range the object, and to detect the edges of the object. The tactile mode permits the robot to sense when it has contacted an object, where on the arm it has made contact, and provides a three-dimensional image of the shape of the contact impression. The pyroelectric mode will be added to permit the robot arm to detect the proximity of a hot object and to add sensing redundancy to the two other modes. The rf-modes of the sensing skin are presented. These modes employ a highly efficient magnetic material (amorphous metal) in a sensing technique. This results in a flexible sensor array which uses a primarily inductive configuration to permit both capacitive and magnetoinductive sensing of object; thus optimizing performance in both proximity and tactile modes with the same sensing skin. The fundamental operating principles, design particulars, and theoretical models are provided to aid in the description and understanding of this sensor. Test results are also given

Expressive touch: Control of robot emotional expression by touch

In this paper, we present a work on control of robot emotional expression using touch sensing. A tactile Bayesian framework is proposed for recognition of different types of touch gestures. We include a sequential analysis method that, based on the accumulation of evidence from tactile interaction, allows to achieve accurate results for recognition of touch. Input data to our method is obtained from touch sensing, which is an important modality for social robotics. Here, emotion in the robot platform are represented by facial expressions, that are handled by a developed control architecture. We validate our method with experiments on tactile interaction in simulated and real robot environments. Results demonstrate that our proposed method is suitable and accurate for control of robot emotions through interaction with humans using touch sensing. Furthermore, it is demonstrated the potential that touch provides as a non-verbal communication channel for the development of social robots capable to interact with humans

GelSlim: A High-Resolution, Compact, Robust, and Calibrated Tactile-sensing Finger

This work describes the development of a high-resolution tactile-sensing finger for robot grasping. This finger, inspired by previous GelSight sensing techniques, features an integration that is slimmer, more robust, and with more homogeneous output than previous vision-based tactile sensors. To achieve a compact integration, we redesign the optical path from illumination source to camera by combining light guides and an arrangement of mirror reflections. We parameterize the optical path with geometric design variables and describe the tradeoffs between the finger thickness, the depth of field of the camera, and the size of the tactile sensing area. The sensor sustains the wear from continuous use -- and abuse -- in grasping tasks by combining tougher materials for the compliant soft gel, a textured fabric skin, a structurally rigid body, and a calibration process that maintains homogeneous illumination and contrast of the tactile images during use. Finally, we evaluate the sensor's durability along four metrics that track the signal quality during more than 3000 grasping experiments.Comment: RA-L Pre-print. 8 page