Welcome to LiRA, where we focus on developing embodied AI agents. Unlike traditional passive AI systems, we specialize in crafting active AI agents capable of understanding and dynamically interacting with the physical world, much like humans do. Our goal extends to the seamless coexistence and collaboration between humans and robots, envisioning a future where these entities work harmoniously together. Drawing from an interdisciplinary toolkit, our research leverages methodologies from reinforcement learning, machine learning, deep learning, optimization, and probabilistic inference.
@inproceedings{22-braun-IROS,title={{RHH}-{LGP}: {Receding} {Horizon} {And} {Heuristics}-{Based} {Logic}-{Geometric} {Programming} {For} {Task} {And} {Motion} {Planning}},author={Braun, Cornelius V. and Ortiz-Haro, Joaquim and Toussaint, Marc and Oguz, Ozgur S.},booktitle={IEEE IROS},year={2022},}
22-hartmann-TRO
Long-Horizon Multi-Robot Rearrangement Planning for Construction Assembly
Valentin Noah Hartmann , Andreas Orthey , Danny Driess , and 2 more authors
@article{22-hartmann-TRO,title={Long-{Horizon} {Multi}-{Robot} {Rearrangement} {Planning} for {Construction} {Assembly}},author={Hartmann, Valentin Noah and Orthey, Andreas and Driess, Danny and Oguz, Ozgur S. and Toussaint, Marc},journal={IEEE TRO - Transactions on Robotics},year={2022},}
21-schubert-NeurIPS
Learning to Execute: Efficient Learning of Universal Plan-Conditioned Policies in Robotics
Ingmar Schubert , Driess, Danny , Ozgur S. Oguz , and 1 more author
NeurIPS - Conf. on Neural Information Processing Systems, 2021
@article{21-schubert-NeurIPS,title={Learning to {Execute}: {Efficient} {Learning} of {Universal} {Plan}-{Conditioned} {Policies} in {Robotics}},shorttitle={Learning to {Execute}},author={Schubert, Ingmar and {Driess, Danny} and Oguz, Ozgur S. and Toussaint, Marc},journal={NeurIPS - Conf{.} on Neural Information Processing Systems},year={2021},}
Bilkent University Department of Computer Engineering Cankaya 06800 Ankara
