Publications related to A structured prediction approach for robot imitation learning

A Gauss-Bonnet Theorem for Asymptotically Conical Manifolds and Manifolds with Conical Singularities

The purpose of this thesis is to provide an intrinsic proof of a Gauss-Bonnet-Chern formula for complete Riemannian manifolds with finitely many conical singularities and asymptotically conical ends. A geometric invariant is associated to the link of both ...

EPFL2019

Renormalization of quantum field theory on Riemannian manifolds

In this paper, we provide a simple pedagogical proof of the existence of covariant renormalizations in Euclidean perturbative quantum field theory on closed Riemannian manifolds, following the Epstein–Glaser philosophy. We rely on a local method that allow ...

2019

Improved Bounds for Discretization of Langevin Diffusions: Near-Optimal Rates without Convexity

Nicolas Henri Bernard Flammarion

We consider minimizing a nonconvex, smooth function

f

on a Riemannian manifold

\mathcal{M}

. We show that a perturbed version of Riemannian gradient descent algorithm converges to a second-order stationary point (and hence is able to escape saddle point ...

2019

Some regularity results for p-harmonic mappings between Riemannian manifolds

Changyu Guo

Let M be a C-2-smooth Riemannian manifold with boundary and N a complete C-2-smooth Riemannian manifold. We show that each stationary p-harmonic mapping u: M -> N, whose image lies in a compact subset of N, is locally C-1,C-alpha for some alpha is an eleme ...

2019

Bayesian Optimization Meets Riemannian Manifolds in Robot Learning

Sylvain Calinon, Noémie Laure Gwendoline Jaquier

Bayesian optimization (BO) recently became popular in robotics to optimize control parameters and parametric policies in direct reinforcement learning due to its data efficiency and gradient-free approach. However, its performance may be seriously compromi ...

2019

Unsupervised machine learning in atomistic simulations, between predictions and understanding

Michele Ceriotti

Automated analyses of the outcome of a simulation have been an important part of atomistic modeling since the early days, addressing the need of linking the behavior of individual atoms and the collective properties that are usually the final quantity of i ...

2019

An Optimal Planning Framework to Deploy Self-Reconfigurable Modular Robots

Auke Ijspeert, Mehmet Hasan Mutlu, Simon Lukas Hauser, Hala Khodr, Alexandre Bernardino

Self-reconfiguration is a hard problem due to the high dimensionality of self-reconfigurable modular systems. Searchbased approaches offer complete and optimal solutions. However, naive search algorithms cannot directly solve self-reconfiguration tasks in ...

2019

Improving hand and wrist activity detection using tactile sensors and tensor regression methods on Riemannian manifolds

Sylvain Calinon, Noémie Laure Gwendoline Jaquier

Simultaneous and proportional control of a prosthetic hand and wrist is still a controversial issue, although giant steps have lately been made in this direction. In this paper, we study the application of a novel machine learning method to the problem, wi ...

2017

Geometric deep learning: going beyond Euclidean data

Pierre Vandergheynst, Michael Bronstein

Many signal processing problems involve data whose underlying structure is non-Euclidean, but may be modeled as a manifold or (combinatorial) graph. For instance, in social networks, the characteristics of users can be modeled as signals on the vertices of ...

2017

Detecting Anisotropic Inclusions Through EIT

Jan Cristina

We study the evolution equation where is the Dirichlet-Neumann operator of a decreasing family of Riemannian manifolds with boundary . We derive a lower bound for the solution of such an equation, and apply it to a quantitative density estimate for the res ...

Springer2017

A structured prediction approach for robot imitation learning

Graph Chatbot

Chat with Graph Search

A Gauss-Bonnet Theorem for Asymptotically Conical Manifolds and Manifolds with Conical Singularities

Renormalization of quantum field theory on Riemannian manifolds

Improved Bounds for Discretization of Langevin Diffusions: Near-Optimal Rates without Convexity

Some regularity results for p-harmonic mappings between Riemannian manifolds

Bayesian Optimization Meets Riemannian Manifolds in Robot Learning

Unsupervised machine learning in atomistic simulations, between predictions and understanding

An Optimal Planning Framework to Deploy Self-Reconfigurable Modular Robots

Improving hand and wrist activity detection using tactile sensors and tensor regression methods on Riemannian manifolds

Geometric deep learning: going beyond Euclidean data

Detecting Anisotropic Inclusions Through EIT

Some regularity results for p-harmonic mappings between Riemannian manifolds

A Gauss-Bonnet Theorem for Asymptotically Conical Manifolds and Manifolds with Conical Singularities

Unsupervised machine learning in atomistic simulations, between predictions and understanding

Improving hand and wrist activity detection using tactile sensors and tensor regression methods on Riemannian manifolds

Renormalization of quantum field theory on Riemannian manifolds

Bayesian Optimization Meets Riemannian Manifolds in Robot Learning

Improved Bounds for Discretization of Langevin Diffusions: Near-Optimal Rates without Convexity

Geometric deep learning: going beyond Euclidean data

Detecting Anisotropic Inclusions Through EIT

An Optimal Planning Framework to Deploy Self-Reconfigurable Modular Robots