Thesis (M.S.)--Ohio State University, 1982. / Includes bibliographical references.

Specifically equipped and programmed robots are highly successful in controlled industrial environments such as automated production lines. For the transition of robots from such controlled uniform environments to unconstrained household environments with a large range of conditions and variations, a new paradigm is needed to prepare the robots for deployment. Robots need to be able to quickly adapt to their changing environments and learn on their own how to solve their tasks in novel situations. This dissertation focusses on the aspect of learning to predict the success of two-object means-end actions in a developmental way. E.g. the action of bringing one object into reach by pulling another, where the one object is on top of the other. Here it is the “on top” relation that affects the success of the action. Learning the preconditions for complex means-end actions via supervised learning can take several thousand training samples, which is impractical to generate, hence more rapid learning capabilities are necessary. Three contributions of this dissertation are used to solve the learning problem. 1. Inspired by infant psychology this dissertation investigates an approach to intrinsic motivation that is based on active learning, guiding the robot's exploration to create experience useful for improving classification performance. 2. This dissertation introduces histogram based 3D vision features that encode the relative spatial relations between surface points of object pairs, allowing a robot to reliably recognise the important spatial categories that affect means-end action outcomes. 3. Intrinsically encoded experience is extracted into symbolic category knowledge, encoding higher level abstract categories. These symbolic categories are used for knowledge transfer by using them to extend the state space of action precondition learning classifiers. Depending on the actions and their preconditions, the contributions of this dissertation enable a robot to achieve success prediction accuracies above 85% with ten training samples instead of approximately 1000 training samples that would otherwise be required. These results can be achieved when (a) the action preconditions can be easily identified from the used vision features or (b) the action preconditions to be learnt rest upon already existing knowledge, then it is possible to achieve these results by reusing the existing knowledge. This dissertation demonstrates, in simulation, an alternative to handcoding the knowledge required for a robot to interact with and manipulate objects in the environment. It shows that rapid learning, grounded in autonomous exploration, can be feasible if the necessary vision features are constructed and if existing knowledge is consistently reused.

by Joseph Chun-kong Chan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1998. / Includes bibliographical references (leaves 117-124). / Abstract also in Chinese. / Abstract --- p.i / Acknowledgments --- p.iv / List of Figures --- p.xi / List of Tables --- p.xii / List of Algorithms --- p.xiii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivation --- p.1 / Chapter 1.2 --- Related Work --- p.5 / Chapter 1.3 --- Contributions --- p.7 / Chapter 1.4 --- Organization of the Thesis --- p.9 / Chapter 2 --- Contact Modeling: Kinematics --- p.11 / Chapter 2.1 --- Introduction --- p.11 / Chapter 2.2 --- Contact Kinematics between Two Rigid Bodies --- p.14 / Chapter 2.2.1 --- Contact Modes --- p.14 / Chapter 2.2.2 --- Montana's Contact Equations --- p.15 / Chapter 2.3 --- Finger Kinematics --- p.18 / Chapter 2.3.1 --- Finger Forward Kinematics --- p.19 / Chapter 2.3.2 --- Finger Jacobian --- p.21 / Chapter 2.4 --- Grasp Kinematics between a Finger and an Object --- p.21 / Chapter 2.4.1 --- Velocity Transformation between Different Coordinate Frames --- p.22 / Chapter 2.4.2 --- Grasp Kinematics for the zth Contact --- p.23 / Chapter 2.4.3 --- Different Fingertip Models and Different Contact Modes --- p.25 / Chapter 2.5 --- Velocity Constraints of the Entire System --- p.28 / Chapter 2.6 --- Summary --- p.29 / Chapter 3 --- Contact Modeling: Dynamics --- p.31 / Chapter 3.1 --- Introduction --- p.31 / Chapter 3.2 --- Multi-fingered Robot Hand Dynamics --- p.33 / Chapter 3.3 --- Object Dynamics --- p.35 / Chapter 3.4 --- Constrained System Dynamics --- p.37 / Chapter 3.5 --- Summary --- p.39 / Chapter 4 --- Collision Modeling --- p.40 / Chapter 4.1 --- Introduction --- p.40 / Chapter 4.2 --- Assumptions of Collision --- p.42 / Chapter 4.3 --- Collision Point Velocities --- p.43 / Chapter 4.3.1 --- Collision Point Velocity of the ith. Finger --- p.43 / Chapter 4.3.2 --- Collision Point Velocity of the Object --- p.46 / Chapter 4.3.3 --- Relative Collision Point Velocity --- p.47 / Chapter 4.4 --- Equations of Collision --- p.47 / Chapter 4.4.1 --- Sliding Mode Collision --- p.48 / Chapter 4.4.2 --- Sticking Mode Collision --- p.49 / Chapter 4.5 --- Summary --- p.51 / Chapter 5 --- Dynamic Simulation --- p.53 / Chapter 5.1 --- Introduction --- p.53 / Chapter 5.2 --- Architecture of the Dynamic Simulation System --- p.54 / Chapter 5.2.1 --- Input Devices --- p.54 / Chapter 5.2.2 --- Dynamic Simulator --- p.58 / Chapter 5.2.3 --- Virtual Environment --- p.60 / Chapter 5.3 --- Methodologies and Program Flow of the Dynamic Simulator --- p.60 / Chapter 5.3.1 --- Interference Detection --- p.61 / Chapter 5.3.2 --- Constraint-based Simulation --- p.63 / Chapter 5.3.3 --- Impulse-based Simulation --- p.66 / Chapter 5.4 --- Summary --- p.69 / Chapter 6 --- Simulation Results --- p.71 / Chapter 6.1 --- Introduction --- p.71 / Chapter 6.2 --- Change of Grasping Configurations --- p.71 / Chapter 6.3 --- Rolling Contact --- p.76 / Chapter 6.4 --- Sliding Contact --- p.76 / Chapter 6.5 --- Collisions --- p.85 / Chapter 6.6 --- Dextrous Manipulation Motions --- p.93 / Chapter 6.7 --- Summary --- p.94 / Chapter 7 --- Conclusions --- p.99 / Chapter 7.1 --- Summary of Contributions --- p.99 / Chapter 7.2 --- Future Work --- p.100 / Chapter 7.2.1 --- Improvement of Current System --- p.100 / Chapter 7.2.2 --- Applications --- p.101 / Chapter A --- Montana's Contact Equations for Finger-object Contact --- p.103 / Chapter A.1 --- Local Coordinates Charts --- p.103 / Chapter A.2 --- "Curvature, Torsion and Metric Tensors" --- p.104 / Chapter A.3 --- Montana's Contact Equations --- p.106 / Chapter B --- Finger Dynamics --- p.108 / Chapter B.1 --- Forward Kinematics of a Robot Finger --- p.108 / Chapter B.1.1 --- Link-coordinate Transformation --- p.109 / Chapter B.1.2 --- Forward Kinematics --- p.109 / Chapter B.2 --- Dynamic Equation of a Robot Finger --- p.110 / Chapter B.2.1 --- Kinetic and Potential Energy --- p.110 / Chapter B.2.2 --- Lagrange's Equation --- p.111 / Chapter C --- Simulation Configurations --- p.113 / Chapter C.1 --- Geometric models --- p.113 / Chapter C.2 --- Physical Parameters --- p.113 / Chapter C.3 --- Simulation Parameters --- p.116 / Bibliography --- p.124

Thesis (M.S.)--University of Missouri-Columbia, 2004. / Typescript. Includes bibliographical references (leaves 110-113). Also available on the Internet.

Thesis (M.S.)--University of Missouri-Columbia, 2004. / Typescript. Includes bibliographical references (leaves 110-113). Also available on the Internet.

D.Ing. / A simulation system of an industrial robot, within a manufacturing environment for its intelligent interaction within the cell as well as its control via the Internet is presented. The simulation verification in an experimental cell in which an ABB lRB 2400 robot operates is discussed. Sensors employed throughout the cell to supply the input for robot action through an expert system are described. The robot interacts with several task groups of the cell, production equipment, materials handling and assembly. The cell use of a PC, directly linked to the robot and other equipment and sensors for, cell control is explained. The PC has full on-line control of all equipment while the simulation runs simultaneously with the experimental set-up. The system incorporates robot and cell control via the Internet. To add additional intelligence to the cell a transponder system, tagging each part in the robotic cell, is also implemented. This enables each part to be identified by the robot, as well as for the robot to interact with each transponder.