1 code implementation • 2 May 2024 • Andrej Orsula, Matthieu Geist, Miguel Olivares-Mendez, Carol Martinez
We demonstrate the adaptability of our agents to novel scenarios and assembly sequences while emphasizing the potential of leveraging advanced simulation techniques for robot learning in space.
no code implementations • 1 Mar 2024 • Junlin Song, Antoine Richard, Miguel Olivares-Mendez
Yet, to work optimally, these functionalities require having accurate and reliable spatial-temporal calibration parameters between the camera and the global pose sensor.
1 code implementation • 6 Oct 2023 • Matteo El-Hariry, Antoine Richard, Vivek Muralidharan, Baris Can Yalcin, Matthieu Geist, Miguel Olivares-Mendez
This investigation introduces a novel deep reinforcement learning-based suite to control floating platforms in both simulated and real-world environments.
1 code implementation • 16 Sep 2023 • Antoine Richard, Junnosuke Kamohara, Kentaro Uno, Shreya Santra, Dave van der Meer, Miguel Olivares-Mendez, Kazuya Yoshida
Trained on our synthetic data, a yolov8 model achieves performance close to a model trained on real-world data, with 5% performance gap.
no code implementations • 18 Aug 2022 • Leo Pauly, Michele Lynn Jamrozik, Miguel Ortiz del Castillo, Olivia Borgue, Inder Pal Singh, Mohatashem Reyaz Makhdoomi, Olga-Orsalia Christidi-Loumpasefski, Vincent Gaudilliere, Carol Martinez, Arunkumar Rathinam, Andreas Hein, Miguel Olivares-Mendez, Djamila Aouada
From the lessons learned during the lab development, this article presents a systematic approach combining market survey and experimental analyses for equipment selection.
1 code implementation • 1 Aug 2022 • Andrej Orsula, Simon Bøgh, Miguel Olivares-Mendez, Carol Martinez
Domain randomization improves the generalization of learned policies to novel scenes with previously unseen objects and different illumination conditions.