no code implementations • 29 Nov 2023 • Noémie Jaquier, Michael C. Welle, Andrej Gams, Kunpeng Yao, Bernardo Fichera, Aude Billard, Aleš Ude, Tamim Asfour, Danica Kragic
Transfer learning is a conceptually-enticing paradigm in pursuit of truly intelligent embodied agents.
no code implementations • 19 Sep 2022 • Alberta Longhini, Marco Moletta, Alfredo Reichlin, Michael C. Welle, David Held, Zackory Erickson, Danica Kragic
We study the problem of learning graph dynamics of deformable objects that generalizes to unknown physical properties.
no code implementations • 14 Sep 2021 • Constantinos Chamzas, Martina Lippi, Michael C. Welle, Anastasia Varava, Lydia E. Kavraki, Danica Kragic
Most methods learn state representations by utilizing losses based on the reconstruction of the raw observations from a lower-dimensional latent space.
no code implementations • 19 Aug 2021 • Michael C. Welle, Petra Poklukar, Danica Kragic
The state-of-the-art unsupervised contrastive visual representation learning methods that have emerged recently (SimCLR, MoCo, SwAV) all make use of data augmentations in order to construct a pretext task of instant discrimination consisting of similar and dissimilar pairs of images.
1 code implementation • 3 Mar 2021 • Martina Lippi, Petra Poklukar, Michael C. Welle, Anastasia Varava, Hang Yin, Alessandro Marino, Danica Kragic
We present a framework for visual action planning of complex manipulation tasks with high-dimensional state spaces, focusing on manipulation of deformable objects.
1 code implementation • 26 Mar 2020 • Thomas Ziegler, Judith Butepage, Michael C. Welle, Anastasiia Varava, Tonci Novkovic, Danica Kragic
Research on automated, image based identification of clothing categories and fashion landmarks has recently gained significant interest due to its potential impact on areas such as robotic clothing manipulation, automated clothes sorting and recycling, and online shopping.
1 code implementation • 19 Mar 2020 • Martina Lippi, Petra Poklukar, Michael C. Welle, Anastasiia Varava, Hang Yin, Alessandro Marino, Danica Kragic
We present a framework for visual action planning of complex manipulation tasks with high-dimensional state spaces such as manipulation of deformable objects.