Publications related to Variational Inference with Mixture Model Approximation for Applications in Robotics

Higher Order Asymptotics: Applications to Satellite Conjunction and Boundary Problems

Higher-order asymptotics provide accurate approximations for use in parametric statistical modelling. In this thesis, we investigate using higher-order approximations in two-specific settings, with a particular emphasis on the tangent exponential model. Th ...

EPFL2023

Memory of Motion for Initializing Optimization in Robotics

Teguh Santoso Lembono

Many robotics problems are formulated as optimization problems. However, most optimization solvers in robotics are locally optimal and the performance depends a lot on the initial guess. For challenging problems, the solver will often get stuck at poor loc ...

EPFL2022

Safe Motion Planning against Multimodal Distributions Based on a Scenario Approach

Maryam Kamgarpour

We present the design of a motion planning algorithm that ensures safety for an autonomous vehicle. In particular, we consider a multimodal distribution over uncertainties; for example, the uncertain predictions of future trajectories of surrounding vehicl ...

2022

Learning from demonstration using products of experts: Applications to manipulation and task prioritization

Sylvain Calinon, Emmanuel Pignat

Probability distributions are key components of many learning from demonstration (LfD) approaches, with the spaces chosen to represent tasks playing a central role. Although the robot configuration is defined by its joint angles, end-effector poses are oft ...

SAGE PUBLICATIONS LTD2021

Product of experts for robot learning from demonstration

Emmanuel Pignat

Adaptability and ease of programming are key features necessary for a wider spread of robotics in factories and everyday assistance. Learning from demonstration (LfD) is an approach to address this problem. It aims to develop algorithms and interfaces such ...

EPFL2021

System and methods for differential imaging using a lock-in camera

Christophe Moser, Timothé Laforest, Damien Claude-Marie Loterie, Chiara Bonati

The present invention describes an imaging system that allows visualization of a wide range of samples both in terms of morphology and in terms of material (e.g. density distribution, varying chemical composition, or anything that induces a change of optic ...

2021

Learning Constrained Distributions of Robot Configurations with Generative Adversarial Network

Sylvain Calinon, Julius Maximilian Jankowski, Emmanuel Pignat, Teguh Santoso Lembono

In high dimensional robotic system, the manifold of the valid configuration space often has a complex shape, especially under constraints such as end-effector orientation or static stability. We propose a generative adversarial network approach to learn th ...

IEEE2021

Scalable Stochastic Optimization: Scenario Reduction with Guarantees

Kilian Schindler

Stochastic optimization is a popular modeling paradigm for decision-making under uncertainty and has a wide spectrum of applications in management science, economics and engineering. However, the stochastic optimization models one faces in practice are int ...

EPFL2020

Collaborative Sampling in Generative Adversarial Networks

Alexandre Massoud Alahi, Yuejiang Liu, Parth Ashit Kothari

The standard practice in Generative Adversarial Networks (GANs) discards the discriminator during sampling. However, this sampling method loses valuable information learned by the discriminator regarding the data distribution. In this work, we propose a co ...

2020

Sublinear Bounds on the Distinguishing Advantage for Multiple Samples

Serge Vaudenay

The maximal achievable advantage of a (computationally unbounded) distinguisher to determine whether a source

Z

is distributed according to distribution

P_0

or

P_1

, when given access to one sample of

Z

, is characterized by the statistical distance ...

Springer, Cham2020

Variational Inference with Mixture Model Approximation for Applications in Robotics

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Higher Order Asymptotics: Applications to Satellite Conjunction and Boundary Problems

Memory of Motion for Initializing Optimization in Robotics

Safe Motion Planning against Multimodal Distributions Based on a Scenario Approach

Learning from demonstration using products of experts: Applications to manipulation and task prioritization

Product of experts for robot learning from demonstration

System and methods for differential imaging using a lock-in camera

Learning Constrained Distributions of Robot Configurations with Generative Adversarial Network

Scalable Stochastic Optimization: Scenario Reduction with Guarantees

Collaborative Sampling in Generative Adversarial Networks

Sublinear Bounds on the Distinguishing Advantage for Multiple Samples

Higher Order Asymptotics: Applications to Satellite Conjunction and Boundary Problems

Scalable Stochastic Optimization: Scenario Reduction with Guarantees

Sublinear Bounds on the Distinguishing Advantage for Multiple Samples

Memory of Motion for Initializing Optimization in Robotics

Product of experts for robot learning from demonstration

Collaborative Sampling in Generative Adversarial Networks

Learning Constrained Distributions of Robot Configurations with Generative Adversarial Network

Safe Motion Planning against Multimodal Distributions Based on a Scenario Approach

Learning from demonstration using products of experts: Applications to manipulation and task prioritization

System and methods for differential imaging using a lock-in camera