Towards a universal end effector : the design and development of production technology's intelligent robot hand : a thesis presented in partial fulfilment of the requirements for the degree of Master of Technology in Engineering and Automation at Massey University

Research into robot hands for industrial use began in the early 1980s and there are now many examples of robot hands in existence. The reason for research into robot hands is that standard robot end effectors have to be designed for each application and are therefore costly. A universal end effector is needed that will be able to perform any parts handling operation or use other tools for other industrial operations. Existing robot hand research would therefore benefit from new concepts, designs and control systems. The Department of Production Technology is developing an intelligent robot hand of a novel configuration, with the ultimate aim of producing a universal end effector. The concept of PTIRH (Production Technology's Intelligent Robot Hand) is that it is a multi-fingered manipulator with a configuration of two thumbs and two fingers. Research by the author for this thesis concentrated on five major areas. First, the background research into the state of the art in robot hand research. Second, the initiation, development and analysis of the novel configuration concept of PTIRH. Third, specification, testing and analysis of air muscle actuation, including design, development and testing of a servo pneumatic control valve for the air muscles. Fourth, choice of sensors for the robot hand, including testing and analysis of two custom made air pressure sensors. Fifth, definition, design, construction, development, testing and analysis of the mechanical structure for an early prototype of PTIRH. Development of an intelligent controller for PTIRH was outside the scope of the author's research. The results of the analysis on the air muscles showed that they could be a suitable direct drive actuator for an intelligent robotic hand. The force, pressure and position sensor results indicate that the sensors could form the basis of the feedback loop for an intelligent controller. The configuration of PTIRH enables it to grasp objects with little reliance on friction. This was demonstrated with an early prototype of the robot hand, which had one finger with actuation and three other static digits, by successfully manually arranging the digits into stable grasps of various objects

"Sticky Hands": learning and generalization for cooperative physical interactions with a humanoid robot

"Sticky Hands" is a physical game for two people involving gentle contact with the hands. The aim is to develop relaxed and elegant motion together, achieve physical sensitivity-improving reactions, and experience an interaction at an intimate yet comfortable level for spiritual development and physical relaxation. We developed a control system for a humanoid robot allowing it to play Sticky Hands with a human partner. We present a real implementation including a physical system, robot control, and a motion learning algorithm based on a generalizable intelligent system capable itself of generalizing observed trajectories' translation, orientation, scale and velocity to new data, operating with scalable speed and storage efficiency bounds, and coping with contact trajectories that evolve over time. Our robot control is capable of physical cooperation in a force domain, using minimal sensor input. We analyze robot-human interaction and relate characteristics of our motion learning algorithm with recorded motion profiles. We discuss our results in the context of realistic motion generation and present a theoretical discussion of stylistic and affective motion generation based on, and motivating cross-disciplinary research in computer graphics, human motion production and motion perception

Learning at the Ends: From Hand to Tool Affordances in Humanoid Robots

One of the open challenges in designing robots that operate successfully in the unpredictable human environment is how to make them able to predict what actions they can perform on objects, and what their effects will be, i.e., the ability to perceive object affordances. Since modeling all the possible world interactions is unfeasible, learning from experience is required, posing the challenge of collecting a large amount of experiences (i.e., training data). Typically, a manipulative robot operates on external objects by using its own hands (or similar end-effectors), but in some cases the use of tools may be desirable, nevertheless, it is reasonable to assume that while a robot can collect many sensorimotor experiences using its own hands, this cannot happen for all possible human-made tools. Therefore, in this paper we investigate the developmental transition from hand to tool affordances: what sensorimotor skills that a robot has acquired with its bare hands can be employed for tool use? By employing a visual and motor imagination mechanism to represent different hand postures compactly, we propose a probabilistic model to learn hand affordances, and we show how this model can generalize to estimate the affordances of previously unseen tools, ultimately supporting planning, decision-making and tool selection tasks in humanoid robots. We present experimental results with the iCub humanoid robot, and we publicly release the collected sensorimotor data in the form of a hand posture affordances dataset.Comment: dataset available at htts://vislab.isr.tecnico.ulisboa.pt/, IEEE International Conference on Development and Learning and on Epigenetic Robotics (ICDL-EpiRob 2017

Educational features of Malaysian robot contest

The educational experiences from robot contest of entry, junior and advance level are presented based on guided constructionism approach in education that combines hands on guidance with hands-on experience. The aim of the competition as a whole are to allow the student to (i) conceptualise the robot (ii) manage the non-deterministic characteristic of the environment and (iii)manage integrated hardware and software development projects. Indeed with this knowledge the student should be able to win a number of international robot tournaments

Design and development of robust hands for humanoid robots

Design and development of robust hands for humanoid robot

The Anthropomorphic Hand Assessment Protocol (AHAP)

The progress in the development of anthropomorphic hands for robotic and prosthetic applications has not been followed by a parallel development of objective methods to evaluate their performance. The need for benchmarking in grasping research has been recognized by the robotics community as an important topic. In this study we present the Anthropomorphic Hand Assessment Protocol (AHAP) to address this need by providing a measure for quantifying the grasping ability of artificial hands and comparing hand designs. To this end, the AHAP uses 25 objects from the publicly available Yale-CMU-Berkeley Object and Model Set thereby enabling replicability. It is composed of 26 postures/tasks involving grasping with the eight most relevant human grasp types and two non-grasping postures. The AHAP allows to quantify the anthropomorphism and functionality of artificial hands through a numerical Grasping Ability Score (GAS). The AHAP was tested with different hands, the first version of the hand of the humanoid robot ARMAR-6 with three different configurations resulting from attachment of pads to fingertips and palm as well as the two versions of the KIT Prosthetic Hand. The benchmark was used to demonstrate the improvements of these hands in aspects like the grasping surface, the grasp force and the finger kinematics. The reliability, consistency and responsiveness of the benchmark have been statistically analyzed, indicating that the AHAP is a powerful tool for evaluating and comparing different artificial hand designs

Sensors for Robotic Hands: A Survey of State of the Art

Recent decades have seen significant progress in the field of artificial hands. Most of the surveys, which try to capture the latest developments in this field, focused on actuation and control systems of these devices. In this paper, our goal is to provide a comprehensive survey of the sensors for artificial hands. In order to present the evolution of the field, we cover five year periods starting at the turn of the millennium. At each period, we present the robot hands with a focus on their sensor systems dividing them into categories, such as prosthetics, research devices, and industrial end-effectors.We also cover the sensors developed for robot hand usage in each era. Finally, the period between 2010 and 2015 introduces the reader to the state of the art and also hints to the future directions in the sensor development for artificial hands

Open Arms: Open-Source Arms, Hands & Control

Open Arms is a novel open-source platform of realistic human-like robotic hands and arms hardware with 28 Degree-of-Freedom (DoF), designed to extend the capabilities and accessibility of humanoid robotic grasping and manipulation. The Open Arms framework includes an open SDK and development environment, simulation tools, and application development tools to build and operate Open Arms. This paper describes these hands controls, sensing, mechanisms, aesthetic design, and manufacturing and their real-world applications with a teleoperated nursing robot. From 2015 to 2022, the authors have designed and established the manufacturing of Open Arms as a low-cost, high functionality robotic arms hardware and software framework to serve both humanoid robot applications and the urgent demand for low-cost prosthetics, as part of the Hanson Robotics Sophia Robot platform. Using the techniques of consumer product manufacturing, we set out to define modular, low-cost techniques for approximating the dexterity and sensitivity of human hands. To demonstrate the dexterity and control of our hands, we present a Generative Grasping Residual CNN (GGR-CNN) model that can generate robust antipodal grasps from input images of various objects in real-time speeds (22ms). We achieved state-of-the-art accuracy of 92.4% using our model architecture on a standard Cornell Grasping Dataset, which contains a diverse set of household objects.Comment: Submitted to 36th Conference on Neural Information Processing Systems (NeurIPS 2022

Compact Tactile Sensors for Robot Fingers

Compact transducer arrays that measure spatial distributions of force or pressure have been demonstrated as prototypes of tactile sensors to be mounted on fingers and palms of dexterous robot hands. The pressure- or force-distribution feedback provided by these sensors is essential for the further development and implementation of robot-control capabilities for humanlike grasping and manipulation

Hand-Gesture Based Programming of Industrial Robot Manipulators

Nowadays, industrial robot manipulators and manufacturing processes are associated as never before. Robot manipulators execute repetitive tasks with increased accuracy and speed, features necessary for industries with needs for manufacturing of products in large quantities by reducing the production time. Although robot manipulators have a significant role for the enhancement of productivity within industries, the programming process of the robot manipulators is an important drawback. Traditional programming methodologies requires robot programming experts and are time consuming. This thesis work aims to develop an application for programming industrial robot manipulators excluding the need of traditional programing methodologies exploiting the intuitiveness of humans’ hands’ gestures. The development of input devices for intuitive Human-Machine Interactions provides the possibility to capture such gestures. Hence, the need of the need of robot manipulator programming experts can be replaced by task experts. In addition, the integration of intuitive means of interaction can reduce be also reduced. The components to capture the hands’ operators’ gestures are a data glove and a precise hand-tracking device. The robot manipulator imitates the motion that human operator performs with the hand, in terms of position. Inverse kinematics are applied to enhance the programming of robot manipulators in-dependently of their structure and manufacturer and researching the possibility for optimizing the programmed robot paths. Finally, a Human-Machine Interface contributes in the programming process by offering important information for the programming process and the status of the integrated components