gabe-yhlee / mmlfd-tutorial Goto Github PK

This repository includes codes for generating figures and videos used in YH's tutoral talk on A Geometric Take on Motion Manifold Learning from Demonstration at Riemann and Gauss meet Asimov: 2nd Tutorial on Geometric Methods in Robot Learning, Optimization and Control in ICRA 2024.

PDF

You can download pre-trained models from HERE. Put these files in the directory MMLfD-Tutorial/results/.

• The Riemannian Geometry of Deep Generative Models (Shao et al., CVPR workshops 2018)
• Latent Space Oddity: on the Curvature of Deep Generative Models (Arvanitidis et al., ICLR 2018)
• Learning Flat Latent Manifolds with VAEs (Chen et al., ICML 2020)
• Geometrically Enriched Latent Spaces (Arvanitidis et al., AISTATS 2021)
• Neighborhood Reconstructing Autoencoders (Lee et al., NeurIPS 2021)
• Pulling back information geometry (Arvanitidis et al., AISTATS 2022)
• Regularized Autoencoders for Isometric Representation Learning (Lee et al., ICLR 2022)
• A Statistical Manifold Framework for Point Cloud Data (Lee et al., ICML 2022)
• Geometric Autoencoders – What You See is What You Decode (Nazari., ICML 2023)
• On Explicit Curvature Regularization of Deep Generative Models (Lee et al., TAG-ML 2023)
• Geometrically regularized autoencoders for non-Euclidean data (Jang et al., ICLR 2023)

• Task-Conditioned Variational Autoencoders for Learning Movement Primitives (Noseworthy et al., CoRL 2019)
• Equivariant Motion Manifold Primitives (Lee et al., CoRL 2023)
• MMP++: Motion Manifold Primitives with Parametric Curve Models (Lee et al., Arxiv 2024)