14 citations · 24 across the 12 of their papers we have counts for
12 papers
Hyperbolic Delaunay Geometric Alignment
Aniss Aiman Medbouhi, Giovanni Luca Marchetti, Vladislav Polianskii +4
Hyperbolic machine learning is an emerging field aimed at representing data with a hierarchical structure. However, there is a lack of tools for evaluation and analysis of the resu…
Training and Evaluation of Deep Policies using Reinforcement Learning and Generative Models
Ali Ghadirzadeh, Petra Poklukar, Karol Arndt +4
We present a data-efficient framework for solving sequential decision-making problems which exploits the combination of reinforcement learning (RL) and latent variable generative m…
Augment-Connect-Explore: a Paradigm for Visual Action Planning with Data Scarcity
Martina Lippi, Michael C. Welle, Petra Poklukar +2
Visual action planning particularly excels in applications where the state of the system cannot be computed explicitly, such as manipulation of deformable objects, as it enables pl…
Delaunay Component Analysis for Evaluation of Data Representations
Petra Poklukar, Vladislav Polianskii, Anastasia Varava +2
Advanced representation learning techniques require reliable and general evaluation methods. Recently, several algorithms based on the common idea of geometric and topological anal…
GraphDCA -- a Framework for Node Distribution Comparison in Real and Synthetic Graphs
Ciwan Ceylan, Petra Poklukar, Hanna Hultin +3
We argue that when comparing two graphs, the distribution of node structural features is more informative than global graph statistics which are often used in practice, especially…
Geometric Multimodal Contrastive Representation Learning
Petra Poklukar, Miguel Vasco, Hang Yin +3
Learning representations of multimodal data that are both informative and robust to missing modalities at test time remains a challenging problem due to the inherent heterogeneity…
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…
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…
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…
Data-efficient visuomotor policy training using reinforcement learning and generative models
Ali Ghadirzadeh, Petra Poklukar, Ville Kyrki +2
We present a data-efficient framework for solving visuomotor sequential decision-making problems which exploits the combination of reinforcement learning (RL) and latent variable g…
Latent Space Roadmap for Visual Action Planning of Deformable and Rigid Object Manipulation
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 such as manipulation of deformable objects. Planning is performed…