2 citations · 4 across the 7 of their papers we have counts for
7 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…
A Robotic Skill Learning System Built Upon Diffusion Policies and Foundation Models
Nils Ingelhag, Jesper Munkeby, Jonne van Haastregt +3
In this paper, we build upon two major recent developments in the field, Diffusion Policies for visuomotor manipulation and large pre-trained multimodal foundational models to obta…
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…
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…
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…
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…