no code implementations • 11 Apr 2024 • Pavel Smirnov, Frank Joublin, Antonello Ceravola, Michael Gienger
Large Language Models (LLMs) are capable of transforming natural language domain descriptions into plausibly looking PDDL markup.
no code implementations • 19 Mar 2024 • Daniel Tanneberg, Felix Ocker, Stephan Hasler, Joerg Deigmoeller, Anna Belardinelli, Chao Wang, Heiko Wersing, Bernhard Sendhoff, Michael Gienger
In addition to following user instructions, Attentive Support is capable of deciding when and how to support the humans, and when to remain silent to not disturb the group.
no code implementations • 11 Oct 2023 • Frank Joublin, Antonello Ceravola, Pavel Smirnov, Felix Ocker, Joerg Deigmoeller, Anna Belardinelli, Chao Wang, Stephan Hasler, Daniel Tanneberg, Michael Gienger
In the pursuit of fully autonomous robotic systems capable of taking over tasks traditionally performed by humans, the complexity of open-world environments poses a considerable challenge.
no code implementations • 9 Aug 2023 • Daniel Tanneberg, Michael Gienger
Symbolic planning is a powerful technique to solve complex tasks that require long sequences of actions and can equip an intelligent agent with complex behavior.
no code implementations • 10 May 2023 • Frank Joublin, Antonello Ceravola, Joerg Deigmoeller, Michael Gienger, Mathias Franzius, Julian Eggert
Large language models (LLMs) have recently become a popular topic in the field of Artificial Intelligence (AI) research, with companies such as Google, Amazon, Facebook, Amazon, Tesla, and Apple (GAFA) investing heavily in their development.
no code implementations • 8 Mar 2023 • Yulei Qiu, Jihong Zhu, Cosimo Della Santina, Michael Gienger, Jens Kober
Deformable Object Manipulation (DOM) is an important field of research as it contributes to practical tasks such as automatic cloth handling, cable routing, surgical operation, etc.
no code implementations • 13 Oct 2022 • Christopher E. Mower, Theodoros Stouraitis, João Moura, Christian Rauch, Lei Yan, Nazanin Zamani Behabadi, Michael Gienger, Tom Vercauteren, Christos Bergeles, Sethu Vijayakumar
However, there is a lack of software connecting reliable contact simulation with the larger robotics ecosystem (i. e. ROS, Orocos), for a more seamless application of novel approaches, found in the literature, to existing robotic hardware.
no code implementations • 6 Dec 2021 • Julien Brosseit, Benedikt Hahner, Fabio Muratore, Michael Gienger, Jan Peters
However, these methods are notorious for the enormous amount of required training data which is prohibitively expensive to collect on real robots.
no code implementations • 1 Nov 2021 • Fabio Muratore, Fabio Ramos, Greg Turk, Wenhao Yu, Michael Gienger, Jan Peters
The rise of deep learning has caused a paradigm shift in robotics research, favoring methods that require large amounts of data.
no code implementations • 18 Oct 2021 • Shen Li, Theodoros Stouraitis, Michael Gienger, Sethu Vijayakumar, Julie A. Shah
Consistent state estimation is challenging, especially under the epistemic uncertainties arising from learned (nonlinear) dynamic and observation models.
no code implementations • 5 Mar 2020 • Fabio Muratore, Christian Eilers, Michael Gienger, Jan Peters
Domain randomization methods tackle this problem by randomizing the physics simulator (source domain) during training according to a distribution over domain parameters in order to obtain more robust policies that are able to overcome the reality gap.
no code implementations • 10 Jul 2019 • Fabio Muratore, Michael Gienger, Jan Peters
Optimizing a policy on a slightly faulty simulator can easily lead to the maximization of the `Simulation Optimization Bias` (SOB).