Search Results for author: Christian Smith

Found 7 papers, 2 papers with code

Combining Planning and Learning of Behavior Trees for Robotic Assembly

1 code implementation16 Mar 2021 Jonathan Styrud, Matteo Iovino, Mikael Norrlöf, Mårten Björkman, Christian Smith

Industrial robots can solve very complex tasks in controlled environments, but modern applications require robots able to operate in unpredictable surroundings as well.

Industrial Robots

Learning Behavior Trees with Genetic Programming in Unpredictable Environments

no code implementations6 Nov 2020 Matteo Iovino, Jonathan Styrud, Pietro Falco, Christian Smith

Modern industrial applications require robots to be able to operate in unpredictable environments, and programs to be created with a minimal effort, as there may be frequent changes to the task.

A Survey of Behavior Trees in Robotics and AI

no code implementations12 May 2020 Matteo Iovino, Edvards Scukins, Jonathan Styrud, Petter Ögren, Christian Smith

Behavior Trees (BTs) were invented as a tool to enable modular AI in computer games, but have received an increasing amount of attention in the robotics community in the last decade.

Reinforcement Learning for Pivoting Task

1 code implementation1 Mar 2017 Rika Antonova, Silvia Cruciani, Christian Smith, Danica Kragic

In this work we propose an approach to learn a robust policy for solving the pivoting task.

Continuous Control Friction +2

A good space: Lexical predictors in word space evaluation

no code implementations LREC 2012 Christian Smith, Henrik Danielsson, Arne J{\"o}nsson

We have investigated the effect on summary quality when using various language resources to train a vector space based extraction summarizer.

Text Categorization Word Sense Disambiguation

This also affects the context - Errors in extraction based summaries

no code implementations LREC 2012 Thomas Kaspersson, Christian Smith, Henrik Danielsson, Arne J{\"o}nsson

These results show that the degree of summarization has to be taken into account to minimize the amount of errors by extraction based summarizers.

Dimensionality Reduction

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