Lower body design of the ‘iCub’ a human-baby like crawling robot

The development of robotic cognition and a greater understanding of human cognition form two of the current greatest challenges of science. Within the RobotCub project the goal is the development of an embodied robotic child (iCub) with the physical and ultimately cognitive abilities of a 2frac12 year old human baby. The ultimate goal of this project is to provide the cognition research community with an open human like platform for understanding of cognitive systems through the study of cognitive development. In this paper the design of the mechanisms adopted for lower body and particularly for the leg and the waist are outlined. This is accompanied by discussion on the actuator group realisation in order to meet the torque requirements while achieving the dimensional and weight specifications. Estimated performance measures of the iCub are presented

Управління трудовими ресурсами в закладах та установах освіти як новий соціально-економічний стандарт

The aim of the present paper is to propose that the adoption of a framework of biological development is suitable for the construction of artificial systems. We will argue that a developmental approach does provide unique insights on how to build highly complex and adaptable artificial systems. To illustrate our point, we will use as an example the acquisition of goal-directed reaching. In the initial part of the paper we will outline a) how mechanisms of biological development can be adapted to the artificial world, and b) how this artificial development differs from traditional engineering approaches to robotics. An experiment performed on an artificial system initially controlled by motor reflexes is presented, showing the acquisition of visuo-motor maps for ballistic control of reaching without explicit knowledge of the system's kinematic parameters

Prioritized Optimal Control

Pre-print of the paper presented at Robotics and Automation (ICRA), IEEE International Conference on, Hong Kong, China, 2014This paper presents a new technique to control highly redundant mechanical systems, such as humanoid robots. We take inspiration from two approaches. Prioritized control is a widespread multi-task technique in robotics and animation: tasks have strict priorities and they are satisfied only as long as they do not conflict with any higher-priority task. Optimal control instead formulates an optimization problem whose solution is either a feedback control policy or a feedforward trajectory of control inputs. We introduce strict priorities in multi-task optimal control problems, as an alternative to weighting task errors proportionally to their importance. This ensures the respect of the specified priorities, while avoiding numerical conditioning issues. We compared our approach with both prioritized control and optimal control with tests on a simulated robot with 11 degrees of freedom

Comparison between Two Implementations of iCub's Fingertip

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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

Integration of Action and Language Knowledge: A Roadmap for Developmental Robotics

“This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder." “Copyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.”This position paper proposes that the study of embodied cognitive agents, such as humanoid robots, can advance our understanding of the cognitive development of complex sensorimotor, linguistic, and social learning skills. This in turn will benefit the design of cognitive robots capable of learning to handle and manipulate objects and tools autonomously, to cooperate and communicate with other robots and humans, and to adapt their abilities to changing internal, environmental, and social conditions. Four key areas of research challenges are discussed, specifically for the issues related to the understanding of: 1) how agents learn and represent compositional actions; 2) how agents learn and represent compositional lexica; 3) the dynamics of social interaction and learning; and 4) how compositional action and language representations are integrated to bootstrap the cognitive system. The review of specific issues and progress in these areas is then translated into a practical roadmap based on a series of milestones. These milestones provide a possible set of cognitive robotics goals and test scenarios, thus acting as a research roadmap for future work on cognitive developmental robotics.Peer reviewe

Calibration and comparison of concrete models with respect to experimental data

At the beginning of the 21st century, civil engineers more than ever face the often-contradictory demands for designing larger, safer and more durable structures at a lower cost and in shorter time. Concrete has been used for many centuries as a safe and durable building material. Two of the main advantages of concrete are its high compressive strength and that it can be cast on the construction site into a variety of shapes and sizes. Many different constitutive models have been developed to fulfill the above mentioned requirements and describe/predict the behavior and failure of concrete. The never ending challenge for engineers is to choose and set up the appropriate material model for the modeling of structures or structural elements. Therefore, the primary objective of the present research is to calibrate, validate and compare different constitutive models with respect to an extensive set of experimental data. Depending on the application and availability of data, the expected prediction quality of the available models may vary significantly. The studied material models include the microplane models M4 and M7, the damage plasticity models available in commercial (ATENA) or open source (OOFEM) finite element codes, e.g. the Grassl-Jirasek material model. Moreover, the Lattice-Discrete-Particle- Model (LDPM), implemented in the solver MARS, is utilized. We present a comparison of these models with regard to the number of input parameters, their physical meaning, the ease of calibration and their predictive capabilities by utilizing a large set of experimental data derived from specimens, cast from the same batch. All models are calibrated using three mean value nominal stress-strain curves obtained from a notched three-point bending, uniaxial compression and compression under passive confinement test. The calibrated numerical models are then used to predict the results of the remaining experiments, i.e. 3-point bending tests of 4 sizes with various notch depths, splitting tests of 5 sizes, direct tensions tests and torsion tests. These data then serve to assess the prediction quality of the models

Highly Sensitive Soft Tactile Sensors for an Anthropomorphic Robotic Hand

This paper describes the design and realization of novel tactile sensors based on soft materials and magnetic sensing. In particular, the goal was to realize: 1) soft; 2) robust; 3) small; and 4) low-cost sensors that can be easily fabricated and integrated on robotic devices that interact with the environment. We targeted a number of desired features, the most important being: 1) high sensitivity; 2) low hysteresis; and 3) repeatability. The sensor consists of a silicone body in which a small magnet is immersed; an Hall-effect sensor placed below the silicone body measures the magnetic field generated by the magnet, which changes when the magnet is displaced due to an applied external pressure. Two different versions of the sensor have been manufactured, characterized, and mounted on an anthropomorphic robotic hand. Experiments, in which the hand interacts with real-world objects, are reported