Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

INTRODUCTION: Brief History. Multifingered Hands and Dextrous Manipulation. Outline of the Book. Bibliography. RIGID BODY MOTION: Rigid Body Transformations. Rotational Motion in R3. Rigid Motion in R3. Velocity of a Rigid Body. Wrenches and Reciprocal Screws. MANIPULATOR KINEMATICS: Introduction. Forward Kinematics. Inverse Kinematics. The Manipulator Jacobian. Redundant and Parallel Manipulators. ROBOT DYNAMICS AND CONTROL: Introduction. Lagrange's Equations. Dynamics of Open-Chain Manipulators. Lyapunov Stability Theory. Position Control and Trajectory Tracking. Control of Constrained Manipulators. MULTIFINGERED HAND KINEMATICS: Introduction to Grasping. Grasp Statics. Force-Closure. Grasp Planning. Grasp Constraints. Rolling Contact Kinematics. HAND DYNAMICS AND CONTROL: Lagrange's Equations with Constraints. Robot Hand Dynamics. Redundant and Nonmanipulable Robot Systems. Kinematics and Statics of Tendon Actuation. Control of Robot Hands. NONHOLONOMIC BEHAVIOR IN ROBOTIC SYSTEMS: Introduction. Controllability and Frobenius' Theorem. Examples of Nonholonomic Systems. Structure of Nonholonomic Systems. NONHOLONOMIC MOTION PLANNING: Introduction. Steering Model Control Systems Using Sinusoids. General Methods for Steering. Dynamic Finger Repositioning. FUTURE PROSPECTS: Robots in Hazardous Environments. Medical Applications for Multifingered Hands. Robots on a Small Scale: Microrobotics. APPENDICES: Lie Groups and Robot Kinematics. A Mathematica Package for Screw Calculus. Bibliography. Index Each chapter also includes a Summary, Bibliography, and Exercises

http://www.cds.caltech.edu/%7Emurray/books/MLS/pdf/mls94-complete.pdf

A Mathematical Introduction to Robotic Manipulation

The PASCAL Visual Object Classes Challenge

This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.

Coverage control for mobile sensing networks

A scheme is developed for classifying the types of motion perceived by a humanlike robot. It is assumed that the robot receives visual images of the scene using a perspective system model. Equations, theorems, concepts, clues, etc., relating the objects, their positions, and their motion to their images on the focal plane are presented. >

http://ieeexplore.ieee.org/iel2/815/3148/00100651.pdf

Robot vision

Novel Air-float Tactile Array for Stiffness Characterization in Soft Tissue Palpation

This research proposes an on-line incremental 3D reconstruction framework that can be used on telepresence robots or human robot interaction (HRI). The aim is a low cost solution that enables users to communicate and interact remotely experiencing the benefits of a face-to-face meeting. By exploring computers graphics techniques and spatial audio we intend to induce sensations of being physical in the presence of other people. There is a wide variety of research opportunities including high performance imaging, multi-view video, virtual view synthesis, etc. One fundamental challenge in geometry reconstruction from traditional cameras array is the lack of accuracy in low-texture or repeated pattern region. Our approach explores virtual view synthesis through motion body estimation and hybrid sensors composed by video cameras and a depth camera based on structured-light or time-of-flight. We present a full 3D body reconstruction system that combines visual features and shape-based alignment. Modeling is based on meshes computed from dense depth maps in order lower the data to be processed and create a 3D mesh representation that is independent of view-point.

Incremental 3d body reconstruction framework for robotic telepresence applications

Presented is an approach for shortest path planning in three dimensional space in the presence of convex polyhedra. It is based on a visibility graph which is extended from two dimensional space into three dimensional one. A collineation is introduced for identification of visible edges in the three dimensional visibility graph. Then optimization schemes are used for finding a set of shortest paths via different edges, and the global shortest path is selected from them. Result of a computer simulation is given showing the versatility and efficiency of this approach. >

3D shortest path planning in the presence of polyhedral obstacles

Feasibility of velocity estimation for all terrian ground vehicles using an optical flow algorithm

In this paper we present a state estimation scheme for Unmanned Aircrafts (UAs) utilizing dynamics based models and multi-sensor data fusion. Employing the UA dynamics in estimation can substantially enhance the estimator performance, but obtaining accurate dynamics parameters for each UA is computationally costly and complex. To eliminate these issues, we propose two decoupled Extended Kalman Filters (EKFs), namely the Rotational Decoupled Extended Kalman Filter (RDEKF) and the Translational Decoupled Extended Kalman Filter (TDEKF). The dynamics parameters in these filters are identified in real-time using the Deep Neural Network and the Modified Relay Feedback Test (DNN-MRFT) approach. This approach doesn’t demand prior knowledge of the UA physical parameters, requiring only an Inertial Measurement Unit (IMU) and a positioning system for model classification. Our estimation scheme provides position, velocity and attitude estimates, in addition to smooth lag-free inertial acceleration estimates. We show experimentally the advantages of our approach on trajectory tracking problems that uses low rate position sensors. We also demonstrate how utilizing the estimated acceleration in feedback control can reduce the tracking error of an optimally tuned system by 43%. Moreover, the proposed estimator produces smooth estimates that leads to a reduction of controller action by 6.6%, when compared to kinematic based estimators. We compare the achieved results against other methods that require full prior knowledge of the UA parameters or the noise models, and show advantages in performance and real-time capability.

/pdf/real-time-adaptive-dynamics-based-state-estimation-scheme-1wkdfldg.pdf

Real-Time Adaptive Dynamics Based State Estimation Scheme for Unmanned Aircrafts

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