The aim of this work is the development of a complete software package to exploit a robotic anthropomorphic hand, such as the Allegro Robotic Hand, allowing to easily perform accurate grasps. It includes the Inverse and the Forward Kinematics and allows storing different configurations as well as to perform and store predefined grasps while controlling the movement speed. The software presented in this document allows the easy use of the Allegro Robotic Hand, enhancing its capabilities and allowing further research on grasp theory by facilitating the experimental implementation. The software is designed to work with all the Allegro Hand versions despite their differences. It is an improved alternative to the manufacturer's version, offering a powerful framework for grasping. Besides, it has been designed to easily adapt to other robotic anthropomorphic hands.

Dual-arm manipulation poses many challenges in order to actually perform manipulation tasks with enough efficiency. To cope with this challenges, this work-in-progress describes a system for motion coordination of a mobile anthropomorphic dual-arm robot that focuses on the capability for performing manipulations actions with precision by means of a visual guidance provided by a camera mounted on an articulated head. The proposal presents different control modes, evaluated with real experiments, and discusses the main future research lines.

This paper presents the implementation of grasp quality measures in a real-world robotic setup. Specifically, the largest wrench that a grasp can resist due to perturbations in any direction has been computed considering both the actual contact points and the magnitude of the actual grasping forces. The experiments were done using the Allegro Hand with OptoForce tactile sensors and a Fastrak motion tracking system. In this work, the grasp quality measure was experimentally computed using different objects without needing their detailed geometric models, and numerical results are provided.

Mobile manipulators working in semi-structured human environments need smart vision capabilities to perceive the world and interact with human operators. With this purpose in mind, this paper presents the development of a robotic head module composed of an OAK-D camera mounted on a pan-and-tilt WidowX XM430 structure. The OAK-D camera provides high-resolution images, including stereo vision and depth sensing, and advanced capabilities based on embedded artificial intelligence functions. These features combined with the high-range motion of the WidowX XM430, allow the head module to have advanced visual tracking capabilities. The implementation has been done using ROS (Robot Operating System), which allows the head module to be easily integrated to any mobile manipulator.

This paper describes a procedure to generate valid robot trajectories for a given sequence of points defining a geometric path, which is a quite frequent output of many robot motion planners. The proposed approach takes into account physical constraints of the robot, as the maximum velocity and the maximum acceleration that each joint can reach, and has been implemented in C++ as a general tool that can be used with different robots.

This work presents a multimodal teleoperation framework that makes use of novel tools and techniques, such as: nonlinear teleoperators control, for ensuring position tracking in the presence of variable time-delays; relational positioning, for increasing operator performance on precise movement execution by visually and haptically displaying geometric constraints; and augmented reality, for visually combining real and virtual information in a compelling way. Experimental evidence is presented that validates the aptitude of the different components of the proposed framework.