1 code implementation • 16 Dec 2023 • Daniel Harnack, Christoph Lüth, Lukas Gross, Shivesh Kumar, Frank Kirchner
Generating physical movement behaviours from their symbolic description is a long-standing challenge in artificial intelligence (AI) and robotics, requiring insights into numerical optimization methods as well as into formalizations from symbolic AI and reasoning.
1 code implementation • 31 Jul 2023 • Raghav Soni, Daniel Harnack, Hauke Isermann, Sotaro Fushimi, Shivesh Kumar, Frank Kirchner
Legged locomotion is arguably the most suited and versatile mode to deal with natural or unstructured terrains.
no code implementations • 7 Jul 2023 • Amr Gomaa, Bilal Mahdy, Niko Kleer, Michael Feld, Frank Kirchner, Antonio Krüger
Recent advances in machine learning models allowed robots to identify objects on a perceptual nonsymbolic level (e. g., through sensor fusion and natural language understanding).
no code implementations • 19 May 2023 • Niklas Kueper, Kartik Chari, Judith Bütefür, Julia Habenicht, Su Kyoung Kim, Tobias Rossol, Marc Tabie, Frank Kirchner, Elsa Andrea Kirchner
The aim of this study was to provide a dataset to the research community, particularly for the development of new methods in the asynchronous detection of erroneous events from the EEG.
no code implementations • 24 Feb 2022 • Dirk Heimann, Hans Hohenfeld, Felix Wiebe, Frank Kirchner
In this work, we utilize Quantum Deep Reinforcement Learning as method to learn navigation tasks for a simple, wheeled robot in three simulated environments of increasing complexity.
no code implementations • 4 Feb 2022 • Sadique Adnan Siddiqui, Lisa Gutzeit, Frank Kirchner
In this work, we investigate the influence of labeling methods on the classification of human movements on data recorded using a marker-based motion capture system.
no code implementations • 28 Sep 2021 • Adrian Lubitz, Matias Valdenegro-Toro, Frank Kirchner
With a Convolutional Neural Network Long Short Term Memory (CNN LSTM) on facial images an accuracy of 92% was reached on the test set.
no code implementations • NeurIPS Workshop ICBINB 2020 • Matthias Rosynski, Frank Kirchner, Matias Valdenegro-Toro
It is being proven to what extent the algorithms can be used in the area of Reinforcement learning.
no code implementations • 29 Oct 2020 • Mohandass Muthuraja, Octavio Arriaga, Paul Plöger, Frank Kirchner, Matias Valdenegro-Toro
In this work, we propose the use of Black-box optimization methods to tune the prior/default box scales in Faster R-CNN and SSD, using Bayesian Optimization, SMAC, and CMA-ES.
1 code implementation • 27 Oct 2020 • Octavio Arriaga, Matias Valdenegro-Toro, Mohandass Muthuraja, Sushma Devaramani, Frank Kirchner
In this paper we introduce the Perception for Autonomous Systems (PAZ) software library.
no code implementations • 29 Jul 2020 • Thomas M. Roehr, Daniel Harnack, Hendrik Wöhrle, Felix Wiebe, Moritz Schilling, Oscar Lima, Malte Langosz, Shivesh Kumar, Sirko Straube, Frank Kirchner
In this paper we introduce Q-Rock, a development cycle for the automated self-exploration and qualification of robot behaviors.
no code implementations • 3 Jul 2020 • Swaroop Bhandary K, Nico Hochgeschwender, Paul Plöger, Frank Kirchner, Matias Valdenegro-Toro
Deep learning models are extensively used in various safety critical applications.
no code implementations • 25 May 2020 • Felix Wiebe, Shivesh Kumar, Daniel Harnack, Malte Langosz, Hendrik Wöhrle, Frank Kirchner
Motion planning is a difficult problem in robot control.
1 code implementation • 29 Feb 2020 • Tim von Oehsen, Alexander Fabisch, Shivesh Kumar, Frank Kirchner
We argue that for rigid-body kinematics one of the first proposed machine learning (ML) solutions to inverse kinematics -- distal teaching (DT) -- is actually good enough when combined with differentiable programming libraries and we provide an extensive evaluation and comparison to analytical and numerical solutions.
no code implementations • 5 Jun 2019 • Alexander Fabisch, Christoph Petzoldt, Marc Otto, Frank Kirchner
Furthermore, we will give an outlook on problems that are challenging today but might be solved by machine learning in the future and argue that classical robotics and other approaches from artificial intelligence should be integrated more with machine learning to form complete, autonomous systems.
no code implementations • 13 Mar 2019 • Bilal Wehbe, Marc Hildebrandt, Frank Kirchner
In this work, a framework for on-line learning of robot dynamics is developed to adapt to such changes.
no code implementations • 17 Sep 2018 • Bilal Wehbe, Octavio Arriaga, Mario Michael Krell, Frank Kirchner
Multi-context model learning is crucial for marine robotics where several factors can cause disturbances to the system's dynamics.