From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

https://web.cs.wpi.edu/~rail/files/ArgallEtAlRAS09.pdf

A survey of robot learning from demonstration

Reinforcement learning offers to robotics a framework and set of tools for the design of sophisticated and hard-to-engineer behaviors. Conversely, the challenges of robotic problems provide both inspiration, impact, and validation for developments in reinforcement learning. The relationship between disciplines has sufficient promise to be likened to that between physics and mathematics. In this article, we attempt to strengthen the links between the two research communities by providing a survey of work in reinforcement learning for behavior generation in robots. We highlight both key challenges in robot reinforcement learning as well as notable successes. We discuss how contributions tamed the complexity of the domain and study the role of algorithms, representations, and prior knowledge in achieving these successes. As a result, a particular focus of our paper lies on the choice between model-based and model-free as well as between value-function-based and policy-search methods. By analyzing a simple problem in some detail we demonstrate how reinforcement learning approaches may be profitably applied, and we note throughout open questions and the tremendous potential for future research.

/pdf/reinforcement-learning-in-robotics-a-survey-4w4397qx9i.pdf

Reinforcement learning in robotics: A survey

IEEE Transactions on Pattern Analysis and Machine Intelligence

/pdf/an-introduction-to-reinforcement-learning-e60mjj3y0p.pdf

An Introduction to Reinforcement Learning

For many people, driving is a routine activity where people drive to the same destinations using the same routes on a regular basis. Many drivers, for example, will drive to and from work along a small set of routes, at about the same time every day of the working week. Similarly, although a person may shop on different days or at different times, they will often visit the same grocery store(s). In this paper, we present a novel approach to predicting driver intent that exploits the predictable nature of everyday driving. Our approach predicts a driver's intended route and destination through the use of a probabilistic model learned from observation of their driving habits. We show that by using a low-cost GPS sensor and a map database, it is possible to build a hidden Markov model (HMM) of the routes and destinations used by the driver. Furthermore, we show that this model can be used to make accurate predictions of the driver's destination and route through on-line observation of their GPS position during the trip. We present a thorough evaluation of our approach using a corpus of almost a month of real, everyday driving. Our results demonstrate the effectiveness of the approach, achieving approximately 98% accuracy in most cases. Such high performance suggests that the method can be harnessed for improved safety monitoring, route planning taking into account traffic density, and better trip duration prediction

http://www.ri.cmu.edu/pub_files/2006/0/driver_intent.pdf

Learning to Predict Driver Route and Destination Intent

As we progress towards a world where robots play an integral role in society, a critical problem that remains to be solved is the pickup team challenge; that is, dynamically formed heterogeneous robot teams executing coordinated tasks where little information is known a priori about the tasks, the robots, and the environments in which they would operate. Successful solutions to forming pickup teams would enable researchers to experiment with larger numbers of robots and enable industry to efficiently and cost-effectively integrate new robot technology with existing legacy teams. In this paper, we define the challenge of pickup teams and propose the treasure hunt domain for evaluating the performance of pickup teams. Additionally, we describe a basic implementation of a pickup team that can search and discover treasure in a previously unknown environment. We build on prior approaches in market-based task allocation and plays for synchronized task execution, to allocate roles amongst robots in the pickup team, and to execute synchronized team actions to accomplish the treasure hunt task

/pdf/dynamically-formed-heterogeneous-robot-teams-performing-51tv1i0v0x.pdf

Dynamically formed heterogeneous robot teams performing tightly-coordinated tasks

In an adversarial multi-robot task, such as playing robot soccer, decisions for team and single-robot behaviour must be made quickly to take advantage of short-term fortuitous events. When no such opportunities exist, the team must execute sequences of coordinated team action that increases the likelihood of future opportunities. A hierarchical architecture, called STP, has been developed to control an autonomous team of robots operating in an adversarial environment. STP consists of skills for executing the low-level actions that make up robot behaviour, tactics for determining what skills to execute, and plays for coordinating synchronized activity among team members. The STP architecture combines each of these components to achieve autonomous team control. Moreover, the STP hierarchy allows for fast team response in adversarial environments while carrying out actions with longer goals. This article presents the STP architecture for controlling an autonomous robot team in a dynamic adversarial t...

/pdf/stp-skills-tactics-and-plays-for-multi-robot-control-in-4xr96jbckt.pdf

STP: Skills, tactics, and plays for multi-robot control in adversarial environments:

Learning by demonstration can be a powerful and natural tool for developing robot control policies. That is, instead of tedious hand-coding, a robot may learn a control policy by interacting with a teacher. In this work we present an algorithm for learning by demonstration in which the teacher operates in two phases. The teacher first demonstrates the task to the learner. The teacher next critiques learner performance of the task. This critique is used by the learner to update its control policy. In our implementation we utilize a 1-Nearest Neighbor technique which incorporates both training dataset and teacher critique. Since the teacher critiques performance only, they do not need to guess at an effective critique for the underlying algorithm. We argue that this method is particularly well-suited to human teachers, who are generally better at assigning credit to performances than to algorithms. We have applied this algorithm to the simulated task of a robot intercepting a ball. Our results demonstrate improved performance with teacher critiquing, where performance is measured by both execution success and efficiency.

/pdf/learning-by-demonstration-with-critique-from-a-human-teacher-4jnrb1tuep.pdf

Brett Browning

Papers

A survey of robot learning from demonstration

Learning to Predict Driver Route and Destination Intent

Dynamically formed heterogeneous robot teams performing tightly-coordinated tasks

STP: Skills, tactics, and plays for multi-robot control in adversarial environments:

Learning by demonstration with critique from a human teacher