881 citations · 1.6k across the 66 of their papers we have counts for
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Comparing Reconstruction- and Contrastive-based Models for Visual Task Planning
Constantinos Chamzas, Martina Lippi, Michael C. Welle +3
Learning state representations enables robotic planning directly from raw observations such as images. Most methods learn state representations by utilizing losses based on the rec…
Batch Curation for Unsupervised Contrastive Representation Learning
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…
GeomCA: Geometric Evaluation of Data Representations
Petra Poklukar, Anastasia Varava, Danica Kragic
Evaluating the quality of learned representations without relying on a downstream task remains one of the challenges in representation learning. In this work, we present Geometric…
Graph-based Normalizing Flow for Human Motion Generation and Reconstruction
Wenjie Yin, Hang Yin, Danica Kragic +1
Data-driven approaches for modeling human skeletal motion have found various applications in interactive media and social robotics. Challenges remain in these fields for generating…
Textile Taxonomy and Classification Using Pulling and Twisting
Alberta Longhini, Michael C. Welle, Ioanna Mitsioni +1
Identification of textile properties is an important milestone toward advanced robotic manipulation tasks that consider interaction with clothing items such as assisted dressing, l…
Bayesian Meta-Learning for Few-Shot Policy Adaptation Across Robotic Platforms
Ali Ghadirzadeh, Xi Chen, Petra Poklukar +3
Reinforcement learning methods can achieve significant performance but require a large amount of training data collected on the same robotic platform. A policy trained with expensi…
Graph-based Task-specific Prediction Models for Interactions between Deformable and Rigid Objects
Zehang Weng, Fabian Paus, Anastasiia Varava +3
Capturing scene dynamics and predicting the future scene state is challenging but essential for robotic manipulation tasks, especially when the scene contains both rigid and deform…
Learning Deep Energy Shaping Policies for Stability-Guaranteed Manipulation
Shahbaz Abdul Khader, Hang Yin, Pietro Falco +1
Deep reinforcement learning (DRL) has been successfully used to solve various robotic manipulation tasks. However, most of the existing works do not address the issue of control st…
Enabling Visual Action Planning for Object Manipulation through Latent Space Roadmap
Martina Lippi, Petra Poklukar, Michael C. Welle +4
We present a framework for visual action planning of complex manipulation tasks with high-dimensional state spaces, focusing on manipulation of deformable objects. We propose a Lat…
Interpretability in Contact-Rich Manipulation via Kinodynamic Images
Ioanna Mitsioni, Joonatan Mänttäri, Yiannis Karayiannidis +2
Deep Neural Networks (NNs) have been widely utilized in contact-rich manipulation tasks to model the complicated contact dynamics. However, NN-based models are often difficult to d…