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Learning Trajectory Distributions for Assisted Teleoperation and Path Planning

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Learning Search Strategies from Human Demonstrations

Guillaume Pierre Luc De Chambrier

Decision making and planning with partial state information is a problem faced by all forms of intelligent entities. The formulation of a problem under partial state information leads to an exorbitant set of choices with associated probabilistic outcomes m ...

EPFL2016

Variable Duration Movement Encoding with Minimal Intervention Control

Sylvain Calinon

Programming by Demonstration (PbD) offers a user-friendly way of skill transfer from human to robot. Typically, demonstration data do not contain the control inputs required to reproduce the demonstrated skill. These control commands can be obtained from a ...

2016

Dimensionality Reduction in Dynamic Optimization under Uncertainty

Napat Rujeerapaiboon

Dynamic optimization problems affected by uncertainty are ubiquitous in many application domains. Decision makers typically model the uncertainty through random variables governed by a probability distribution. If the distribution is precisely known, then ...

EPFL2016

Diffusion stochastic optimization with non-smooth regularizers

Ali H. Sayed, Stefan Vlaski

We develop an effective distributed strategy for seeking the Pareto solution of an aggregate cost consisting of regularized risks. The focus is on stochastic optimization problems where each risk function is expressed as the expectation of some loss functi ...

IEEE2016

A Distributed Noise-Resistant Particle Swarm Optimization Algorithm for High-Dimensional Multi-Robot Learning

Alcherio Martinoli, Inaki Navarro Oiza, Ezequiel Leonardo Di Mario

Population-based learning techniques have been proven to be effective in dealing with noise in numerical benchmark functions and are thus promising tools for the high-dimensional optimization of controllers for multiple robots with limited sensing capabili ...

2015

Distributionally Robust Convex Optimization

Daniel Kuhn, Wolfram Wiesemann

Distributionally robust optimization is a paradigm for decision-making under uncertainty where the uncertain problem data is governed by a probability distribution that is itself subject to uncertainty. The distribution is then assumed to belong to an ambi ...

2014

Learning a Real Time Grasping Strategy

Aude Billard, Sahar El Khoury, Miao Li

Real time planning strategy is crucial for robots working in dynamic environments. In particular, robot grasping tasks require quick reactions in many applications such as human-robot interaction. In this paper, we propose an approach for grasp learning th ...

2013

Message-Passing Algorithms: Reparameterizations and Splittings

Nicholas Ruozzi

The max-product algorithm, a local message-passing scheme that attempts to compute the most probable assignment (MAP) of a given probability distribution, has been successfully employed as a method of approximate inference for applications arising in codin ...

Ieee-Inst Electrical Electronics Engineers Inc2013

A Probabilistic Framework for Multiple Speaker Localization

This paper presents a novel probabilistic framework for localizing multiple speakers with a microphone array. In this framework, the generalized cross correlation function (GCC) of each microphone pair is interpreted as a probability distribution of the ti ...

2013

A Probabilistic Framework for Multiple Speaker Localization

Idiap2012