no code implementations • 10 May 2024 • Lasse Shala, Shubham Vyas, Mohamed Khalil Ben-Larbi, Shivesh Kumar, Enrico Stoll
Feasible control architectures should take into consideration the inherent coupling of the free floating dynamics and the kinematics of the system.
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 • 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.
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.