Zobrazeno 1 - 10
of 132
pro vyhledávání: '"Harnack, Daniel"'
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
Externí odkaz:
http://arxiv.org/abs/2312.10328
Autor:
Soni, Raghav, Harnack, Daniel, Isermann, Hannah, Fushimi, Sotaro, Kumar, Shivesh, Kirchner, Frank
Publikováno v:
End-to-End Reinforcement Learning for Torque Based Variable Height Hopping, 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Detroit, MI, USA, 2023, pp. 7531-7538
Legged locomotion is arguably the most suited and versatile mode to deal with natural or unstructured terrains. Intensive research into dynamic walking and running controllers has recently yielded great advances, both in the optimal control and reinf
Externí odkaz:
http://arxiv.org/abs/2307.16676
Autor:
Rausch, Lukas-Paul, Schünemann, Maik, Drebitz, Eric, Harnack, Daniel, Ernst, Udo A., Kreiter, Andreas K.
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. The underlying neural mechanisms are still debated, but computational stu
Externí odkaz:
http://arxiv.org/abs/2305.14153
Autor:
Javadi, Mahdi, Harnack, Daniel, Stocco, Paula, Kumar, Shivesh, Vyas, Shubham, Pizzutilo, Daniel, Kirchner, Frank
Publikováno v:
journal={IEEE Robotics and Automation Letters}, year={2023}, volume={8}, number={6}, pages={3637-3644}
Brachiation is a dynamic, coordinated swinging maneuver of body and arms used by monkeys and apes to move between branches. As a unique underactuated mode of locomotion, it is interesting to study from a robotics perspective since it can broaden the
Externí odkaz:
http://arxiv.org/abs/2305.08373
Quantifying the Effect of Feedback Frequency in Interactive Reinforcement Learning for Robotic Tasks
Reinforcement learning (RL) has become widely adopted in robot control. Despite many successes, one major persisting problem can be very low data efficiency. One solution is interactive feedback, which has been shown to speed up RL considerably. As a
Externí odkaz:
http://arxiv.org/abs/2207.09845
Modeling trajectories generated by robot joints is complex and required for high level activities like trajectory generation, clustering, and classification. Disentagled representation learning promises advances in unsupervised learning, but they hav
Externí odkaz:
http://arxiv.org/abs/2112.03164
Autor:
Roehr, Thomas M., Harnack, Daniel, Wöhrle, Hendrik, Wiebe, Felix, Schilling, Moritz, Lima, Oscar, Langosz, Malte, Kumar, Shivesh, Straube, Sirko, Kirchner, Frank
In this paper we introduce Q-Rock, a development cycle for the automated self-exploration and qualification of robot behaviors. With Q-Rock, we suggest a novel, integrative approach to automate robot development processes. Q-Rock combines several mac
Externí odkaz:
http://arxiv.org/abs/2007.14928
Autor:
Wiebe, Felix, Kumar, Shivesh, Harnack, Daniel, Langosz, Malte, Wöhrle, Hendrik, Kirchner, Frank
Motion planning is a difficult problem in robot control. The complexity of the problem is directly related to the dimension of the robot's configuration space. While in many theoretical calculations and practical applications the configuration space
Externí odkaz:
http://arxiv.org/abs/2005.12064
Autor:
Harnack, Daniel1 (AUTHOR), Pivin-Bachler, Julie2 (AUTHOR), Navarro-Guerrero, Nicolás1 (AUTHOR) nicolas.navarro@dfki.de
Publikováno v:
Neural Computing & Applications. Aug2023, Vol. 35 Issue 23, p16931-16943. 13p.
Discovery of causal relations is fundamental for understanding the dynamics of complex systems. While causal interactions are well defined for acyclic systems that can be separated into causally effective subsystems, a mathematical definition of grad
Externí odkaz:
http://arxiv.org/abs/1605.02570