Journal ArticleDOI
A survey of robot learning from demonstration
TLDR
A comprehensive survey of robot Learning from Demonstration (LfD), a technique that develops policies from example state to action mappings, which analyzes and categorizes the multiple ways in which examples are gathered, as well as the various techniques for policy derivation.About:
This article is published in Robotics and Autonomous Systems.The article was published on 2009-05-01. It has received 3343 citations till now. The article focuses on the topics: Robot learning & Programming by demonstration.read more
Citations
More filters
Proceedings Article
A Reduction from Apprenticeship Learning to Classification
Umar Syed,Robert E. Schapire +1 more
TL;DR: Not only does imitating a near-optimal expert result in a better policy, but far fewer demonstrations are required to successfully imitate such an expert, suggesting an opportunity for substantial savings whenever the expert is known to be good, but demonstrations are expensive or difficult to obtain.
Journal ArticleDOI
Human–Vehicle Cooperation in Automated Driving: A Multidisciplinary Review and Appraisal
TL;DR: A review is made on key studies in human–robot interaction and human factors that will help professionals shape future directions for safer and more efficient and effective human–vehicle cooperation.
Journal ArticleDOI
A Survey of Personalization for Advanced Driver Assistance Systems
TL;DR: A general conceptual framework to personalization in ADAS is proposed which suggests a modular decomposition for the next generation of personalized ADAS and HMI which can be expected to continuously adapt in interaction with the driver.
Proceedings ArticleDOI
Functional object-oriented network for manipulation learning
TL;DR: The paper describes FOON's structure and an approach to form a universal FOON with extracted knowledge from online instructional videos, demonstrating the flexibility of FOON in creating a novel and adaptive means of solving a problem using knowledge gathered from multiple sources.
Posted Content
Improvisation through Physical Understanding: Using Novel Objects as Tools with Visual Foresight
TL;DR: This work training a model with both a visual and physical understanding of multi-object interactions, and develops a sampling-based optimizer that can leverage these interactions to accomplish tasks, shows that the robot can perceive and use novel objects as tools, including objects that are not conventional tools, while also choosing dynamically to use or not use tools depending on whether or not they are required.
References
More filters
Book
Reinforcement Learning: An Introduction
TL;DR: This book provides a clear and simple account of the key ideas and algorithms of reinforcement learning, which ranges from the history of the field's intellectual foundations to the most recent developments and applications.
Book
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
TL;DR: In this paper, the authors describe the important ideas in these areas in a common conceptual framework, and the emphasis is on concepts rather than mathematics, with a liberal use of color graphics.
Journal ArticleDOI
An introduction to variable and feature selection
Isabelle Guyon,André Elisseeff +1 more
TL;DR: The contributions of this special issue cover a wide range of aspects of variable selection: providing a better definition of the objective function, feature construction, feature ranking, multivariate feature selection, efficient search methods, and feature validity assessment methods.
Journal ArticleDOI
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
TL;DR: The Elements of Statistical Learning: Data Mining, Inference, and Prediction as discussed by the authors is a popular book for data mining and machine learning, focusing on data mining, inference, and prediction.
Proceedings ArticleDOI
Apprenticeship learning via inverse reinforcement learning
Pieter Abbeel,Andrew Y. Ng +1 more
TL;DR: This work thinks of the expert as trying to maximize a reward function that is expressible as a linear combination of known features, and gives an algorithm for learning the task demonstrated by the expert, based on using "inverse reinforcement learning" to try to recover the unknown reward function.