Alexander Fabisch

Orcid: 0000-0003-2824-7956

According to our database1, Alexander Fabisch authored at least 16 papers between 2012 and 2022.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

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Article 
PhD thesis 
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Links

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Bibliography

2022
A Modular Approach to the Embodiment of Hand Motions from Human Demonstrations.
CoRR, 2022

A Modular Approach to the Embodiment of Hand Motions from Human Demonstrations.
Proceedings of the 21st IEEE-RAS International Conference on Humanoid Robots, 2022

2021
gmr: Gaussian Mixture Regression.
J. Open Source Softw., 2021

2020
Comparison of Distal Teacher Learning with Numerical and Analytical Methods to Solve Inverse Kinematics for Rigid-Body Mechanisms.
CoRR, 2020

2019
pytransform3d: 3D Transformations for Python.
J. Open Source Softw., 2019

A Survey of Behavior Learning Applications in Robotics - State of the Art and Perspectives.
CoRR, 2019

A Comparison of Policy Search in Joint Space and Cartesian Space for Refinement of Skills.
Proceedings of the Advances in Service and Industrial Robotics, 2019

Automated Robot Skill Learning from Demonstration for Various Robot Systems.
Proceedings of the KI 2019: Advances in Artificial Intelligence, 2019

Empirical evaluation of contextual policy search with a comparison-based surrogate model and active covariance matrix adaptation.
Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2019

2018
The BesMan Learning Platform for Automated Robot Skill Learning.
Frontiers Robotics AI, 2018

2017
Online model identification for underwater vehicles through incremental support vector regression.
Proceedings of the 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2017

2015
Accounting for Task-Difficulty in Active Multi-Task Robot Control Learning.
Künstliche Intell., 2015

2014
Towards Learning of Generic Skills for Robotic Manipulation.
Künstliche Intell., 2014

Active contextual policy search.
J. Mach. Learn. Res., 2014

2013
Learning in compressed space.
Neural Networks, 2013

2012
Learning Parameters of Linear Models in Compressed Parameter Space.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2012, 2012


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