Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

The organization of behavior: A neuropsychological theory

Thank you for downloading the body in the mind the bodily basis of meaning imagination and reason. As you may know, people have search numerous times for their favorite readings like this the body in the mind the bodily basis of meaning imagination and reason, but end up in malicious downloads. Rather than enjoying a good book with a cup of tea in the afternoon, instead they are facing with some harmful bugs inside their desktop computer.

The Body in the Mind: The Bodily Basis of Meaning, Imagination, and Reason

This paper provides a review of the literature in on-road vision-based vehicle detection, tracking, and behavior understanding. Over the past decade, vision-based surround perception has progressed from its infancy into maturity. We provide a survey of recent works in the literature, placing vision-based vehicle detection in the context of sensor-based on-road surround analysis. We detail advances in vehicle detection, discussing monocular, stereo vision, and active sensor-vision fusion for on-road vehicle detection. We discuss vision-based vehicle tracking in the monocular and stereo-vision domains, analyzing filtering, estimation, and dynamical models. We discuss the nascent branch of intelligent vehicles research concerned with utilizing spatiotemporal measurements, trajectories, and various features to characterize on-road behavior. We provide a discussion on the state of the art, detail common performance metrics and benchmarks, and provide perspective on future research directions in the field.

https://cvrr.ucsd.edu/publications/2013/SayananTrivedi_IEEETITS2013_VehicleSurvey.pdf

Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis

We present a whole body motion control algorithm for humanoid robots It is based on the framework of Liegeois and solves the redundant inverse kinematics problem on velocity level We control the hand positions as well as the hand and head attitude The attitude is described with a novel 2-dof description suited for symmetrical problems Task-specific command elements can be assigned to the command vector at any time, such enabling the system to control one or multiple effectors and to seamlessly switch between such modes while generating a smooth, natural motion Further, kinematic constraints can be assigned to individual degrees of freedom The underlying kinematic model does not consider the leg joints explicitly Nevertheless, the method can be used in combination with an independent balance or walking control system, such reducing the complexity of a complete system control We show how to incorporate walking in this control scheme and present experimental results on ASIMO

Task-oriented whole body motion for humanoid robots

Recent advances in the field of humanoid robotics increase the complexity of the tasks that such robots can perform. This makes it increasingly difficult and inconvenient to program these tasks manually. Furthermore, humanoid robots, in contrast to industrial robots, should in the distant future behave within a social environment. Therefore, it must be possible to extend the robot's abilities in an easy and natural way. To address these requirements, this work investigates the topic of imitation learning of motor skills. The focus lies on providing a humanoid robot with the ability to learn new bi-manual tasks through the observation of object trajectories. For this, an imitation learning framework is presented, which allows the robot to learn the important elements of an observed movement task by application of probabilistic encoding with Gaussian Mixture Models. The learned information is used to initialize an attractor-based movement generation algorithm that optimizes the reproduced movement towards the fulfillment of additional criteria, such as collision avoidance. Experiments performed with the humanoid robot ASIMO show that the proposed system is suitable for transferring information from a human demonstrator to the robot. These results provide a good starting point for more complex and interactive learning tasks.

Task-level imitation learning using variance-based movement optimization

We propose a self collision avoidance system that superposes trajectories in order not only to protect the robot's hardware but also to enable continuous motions. The system runs in real-time so that the robot can work in an uncertain environment. It is based on virtual forces between close segments of the robot. The avoidance movements are blended with a whole body motion control in order to change the priority between target reaching and collision avoidance. The blending is performed autonomously without the necessity of external switching. Our method works both while the robot is standing and walking. Reaching motions from the front to the side of the body without the arm colliding with the body are possible. Even if the target is inside the body, the arm stops at the closest point to the target outside of the body. Our method can be used for other applications: We apply it to realizing a "body schema" and for "occlusion avoidance."

/pdf/real-time-collision-avoidance-with-whole-body-motion-control-4apj81fb42.pdf

Real-time collision avoidance with whole body motion control for humanoid robots

Many advanced motion control strategies developed in robotics use captured human motion data as valuable source of examples to simplify the process of programming or learning complex robot motions. Direct and online control of robots from observed human motion has several inherent challenges. The most important may be the representation of the large number of mechanical degrees of freedom involved in the execution of movement tasks. Attempting to map all such degrees of freedom from a human to a humanoid is a formidable task from an instrumentation and sensing point of view. More importantly, such an approach is incompatible with mechanisms in the central nervous system which are believed to organize or simplify the control of these degrees of freedom during motion execution and motor learning phase. Rather than specifying the desired motion of every degree of freedom for the purpose of motion control, it is important to describe motion by low dimensional motion primitives that are defined in Cartesian (or task) space. In this paper, we formulate the human to humanoid retargeting problem as a task space control problem. The control objective is to track desired task descriptors while satisfying constraints such as joint limits, velocity limits, collision avoidance, and balance. The retargeting algorithm generates the joint space trajectories that are commanded to the robot. We present experimental and simulation results of the retargeting control algorithm on the Honda humanoid robot ASIMO.

Whole body humanoid control from human motion descriptors

Transferring motion from a human demonstrator to a humanoid robot is an important step toward developing robots that are easily programmable and that can replicate or learn from observed human motion. The so called motion retargeting problem has been well studied and several off-line solutions exist based on optimization approaches that rely on pre-recorded human motion data collected from a marker-based motion capture system. From the perspective of human robot interaction, there is a growing interest in online motion transfer, particularly without using markers. Such requirements have placed stringent demands on retargeting algorithms and limited the potential use of off-line and pre-recorded methods. To address these limitations, we present an online task space control theoretic retargeting formulation to generate robot joint motions that adhere to the robot's joint limit constraints, joint velocity constraints and self-collision constraints. The inputs to the proposed method include low dimensional normalized human motion descriptors, detected and tracked using a vision based key-point detection and tracking algorithm. The proposed vision algorithm does not rely on markers placed on anatomical landmarks, nor does it require special instrumentation or calibration. The current implementation requires a depth image sequence, which is collected from a single time of flight imaging device. The feasibility of the proposed approach is shown by means of online experimental results on the Honda humanoid robot — ASIMO.

Christian Goerick

Papers

Task-oriented whole body motion for humanoid robots

Task-level imitation learning using variance-based movement optimization

Real-time collision avoidance with whole body motion control for humanoid robots

Whole body humanoid control from human motion descriptors

Online transfer of human motion to humanoids