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
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Journal ArticleDOI
State Abstraction as Compression in Apprenticeship Learning
TL;DR: This work offers the first formalism and analysis of the trade-off between compression and performance made in the context of state abstraction for Apprenticeship Learning and builds on Rate-Distortion theory, the classic Blahut-Arimoto algorithm, and the Information Bottleneck method to develop an algorithm.
Proceedings ArticleDOI
Preference-Based Learning for Exoskeleton Gait Optimization
Maegan Tucker,Ellen Novoseller,Claudia Kann,Yanan Sui,Yisong Yue,Joel W. Burdick,Aaron D. Ames +6 more
TL;DR: In this paper, a personalized gait optimization framework for lower-body exoskeletons is presented, which prompts the user to give pairwise preferences between trials and suggest improvements.
Proceedings ArticleDOI
Combining motion planning and optimization for flexible robot manipulation
Jonathan Scholz,Mike Stilman +1 more
TL;DR: A task-space probabilistic planner which solves general manipulation tasks posed as optimization criteria and is validated in simulation and on a 7-DOF robot arm that executes several tabletop manipulation tasks.
Journal ArticleDOI
Teleoperation with intelligent and customizable interfaces
TL;DR: Intelligent and customizable interfaces are studied: these are interfaces that mediate the consequences of indirectness and make teleoperation more seamless.
Book ChapterDOI
Robobarista: Object Part Based Transfer of Manipulation Trajectories from Crowd-Sourcing in 3D Pointclouds
TL;DR: In this paper, the authors formulate the manipulation planning as a structured prediction problem and design a deep learning model that can handle large noise in the manipulation demonstrations and learns features from three different modalities: point-clouds, language and trajectory.
References
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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.