Search Results for author: Aude Billard

Found 8 papers, 4 papers with code

Learning Dynamical Systems Encoding Non-Linearity within Space Curvature

2 code implementations18 Mar 2024 Bernardo Fichera, Aude Billard

By learning the manifold's Euclidean embedded representation, our approach encodes the non-linearity of the DS within the curvature of the space.

Computational Efficiency

Implicit Manifold Gaussian Process Regression

1 code implementation NeurIPS 2023 Bernardo Fichera, Viacheslav Borovitskiy, Andreas Krause, Aude Billard

Gaussian process regression is widely used because of its ability to provide well-calibrated uncertainty estimates and handle small or sparse datasets.

regression

Pedestrian-Robot Interactions on Autonomous Crowd Navigation: Reactive Control Methods and Evaluation Metrics

1 code implementation3 Aug 2022 Diego Paez-Granados, Yujie He, David Gonon, Dan Jia, Bastian Leibe, Kenji Suzuki, Aude Billard

Autonomous navigation in highly populated areas remains a challenging task for robots because of the difficulty in guaranteeing safe interactions with pedestrians in unstructured situations.

Autonomous Navigation

Linearization and Identification of Multiple-Attractor Dynamical Systems through Laplacian Eigenmaps

no code implementations18 Feb 2022 Bernardo Fichera, Aude Billard

We study the eigenvectors and eigenvalues of the Graph Laplacian and show that they form a set of orthogonal embedding spaces, one for each sub-dynamics.

Clustering

Transform-Invariant Non-Parametric Clustering of Covariance Matrices and its Application to Unsupervised Joint Segmentation and Action Discovery

2 code implementations27 Oct 2017 Nadia Figueroa, Aude Billard

Resulting in a topic-modeling inspired hierarchical model for unsupervised time-series data analysis which we call ICSC-HMM (IBP Coupled SPCM-CRP Hidden Markov Model).

Clustering Time Series +1

Augmented-SVM: Automatic space partitioning for combining multiple non-linear dynamics

no code implementations NeurIPS 2012 Ashwini Shukla, Aude Billard

These new SV ensure that the resulting multi-stable DS incurs minimum deviation from the original dynamics and is stable at each of the attractors within a finite region of attraction.

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