21 citations · 42 across the 7 of their papers we have counts for
7 papers
On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning
Sacha Morin, Moonsub Byeon, Alexia Jolicoeur-Martineau +1
Semi-supervised imitation learning (SSIL) consists in learning a policy from a small dataset of action-labeled trajectories and a much larger dataset of action-free trajectories. S…
Spectral Temporal Contrastive Learning
Sacha Morin, Somjit Nath, Samira Ebrahimi Kahou +1
Learning useful data representations without requiring labels is a cornerstone of modern deep learning. Self-supervised learning methods, particularly contrastive learning (CL), ha…
ConceptGraphs: Open-Vocabulary 3D Scene Graphs for Perception and Planning
Qiao Gu, Alihusein Kuwajerwala, Sacha Morin +13
For robots to perform a wide variety of tasks, they require a 3D representation of the world that is semantically rich, yet compact and efficient for task-driven perception and pla…
StepMix: A Python Package for Pseudo-Likelihood Estimation of Generalized Mixture Models with External Variables
Sacha Morin, Robin Legault, Félix Laliberté +4
StepMix is an open-source Python package for the pseudo-likelihood estimation (one-, two- and three-step approaches) of generalized finite mixture models (latent profile and latent…
One-4-All: Neural Potential Fields for Embodied Navigation
Sacha Morin, Miguel Saavedra-Ruiz, Liam Paull
A fundamental task in robotics is to navigate between two locations. In particular, real-world navigation can require long-horizon planning using high-dimensional RGB images, which…
Monocular Robot Navigation with Self-Supervised Pretrained Vision Transformers
Miguel Saavedra-Ruiz, Sacha Morin, Liam Paull
In this work, we consider the problem of learning a perception model for monocular robot navigation using few annotated images. Using a Vision Transformer (ViT) pretrained with a l…
Extendable and invertible manifold learning with geometry regularized autoencoders
Andrés F. Duque, Sacha Morin, Guy Wolf +1
A fundamental task in data exploration is to extract simplified low dimensional representations that capture intrinsic geometry in data, especially for faithfully visualizing data…