Sarah Osentoski

Publications34
h-index17
Citations1,387
Highly Influential Citations68
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Value Function Approximation in Reinforcement Learning Using the Fourier Basis
TLDR
We describe the Fourier basis, a linear value function approximation scheme based on Fourier series. Expand
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Rosbridge: ROS for Non-ROS Users
TLDR
We present rosbridge, a middleware abstraction layer which provides robotics technology with a standard, minimalist applications development framework accessible to applications programmers who are not themselves roboticists. Expand
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Learning and generalization of complex tasks from unstructured demonstrations
TLDR
We present a novel method for segmenting demonstrations, recognizing repeated skills, and generalizing complex tasks from unstructured demonstrations that combines many of the advantages of recent automatic segmentation methods for learning from demonstration into a single principled, integrated framework. Expand
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Learning grounded finite-state representations from unstructured demonstrations
TLDR
Robots exhibit flexible behavior largely in proportion to their degree of knowledge about the world. Expand
  • 149
  • 7
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Incremental Semantically Grounded Learning from Demonstration
TLDR
We introduce a novel method for discovering semantically grounded primitives and incrementally building and improving a finite-state representation of a task in which various contingencies can arise. Expand
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Learning from demonstration using a multi-valued function regressor for time-series data
TLDR
We propose a multi-valued function regressor to learn a larger class of robot control policies from human demonstration and extend the Hierarchical Dirichlet Process Hidden Markov Model. Expand
  • 47
  • 4
PR2 Remote Lab: An environment for remote development and experimentation
TLDR
We present a remote lab system that allows remote groups to access a shared PR2. Expand
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  • 4
  • PDF
ROS and Rosbridge: Roboticists out of the loop
TLDR
The advent of ROS, the Robot Operating System, has finally made it possible to implement and use state-of-the-art navigation and manipulation algorithms on widely-available, inexpensive standard robot platforms. Expand
  • 42
  • 3
  • PDF
Remote Robotic Laboratories for Learning from Demonstration
TLDR
This paper documents the technology developed during the creation of the PR2 Remote Lab and the process of using it for shared development for Learning from Demonstration. Expand
  • 34
  • 3
  • PDF
Segmentation of Textured and Textureless Objects through Interactive Perception
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  • 3