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 • 23 May 2023 • Lukas-Paul Rausch, Maik Schünemann, Eric Drebitz, Daniel Harnack, Udo A. Ernst, Andreas K. Kreiter
When selective attention is devoted to one of multiple stimuli within receptive fields of neurons in visual area V4, cells respond as if only the attended stimulus was present.
no code implementations • 20 Jul 2022 • Daniel Harnack, Julie Pivin-Bachler, Nicolás Navarro-Guerrero
In the literature, there is no consensus about which feedback frequency is optimal or at which time the feedback is most beneficial.
no code implementations • 6 Dec 2021 • Matias Valdenegro-Toro, Daniel Harnack, Hendrik Wöhrle
Modeling trajectories generated by robot joints is complex and required for high level activities like trajectory generation, clustering, and classification.
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 • 25 May 2020 • Felix Wiebe, Shivesh Kumar, Daniel Harnack, Malte Langosz, Hendrik Wöhrle, Frank Kirchner
Motion planning is a difficult problem in robot control.