Concept

Trial and error

Related publications (8)

RMAML: Riemannian meta-learning with orthogonality constraints

Meta-learning is the core capability that enables intelligent systems to rapidly generalize their prior ex-perience to learn new tasks. In general, the optimization-based methods formalize the meta-learning as a bi-level optimization problem, that is a nes ...

ELSEVIER SCI LTD2023

Hybrid Flock - Formation Control Algorithms

Alcherio Martinoli, Cyrill Silvan Baumann, Jonas Perolini, Emna Tourki

Two prominent categories for achieving coordinated multirobot displacement are flocking and navigation in formation. Both categories have their own body of literature and characteristics, including their respective advantages and disadvantages. While typic ...

2022

Benchmark for Bimanual Robotic Manipulation of Semi-deformable Objects

Aude Billard, Kunpeng Yao, Bernardo Fichera, Farshad Khadivar, Konstantinos Chatzilygeroudis, Ilaria Lauzana, Fanjun Bu

We propose a new benchmarking protocol to evaluate algorithms for bimanual robotic manipulation semi-deformable objects. The benchmark is inspired from two real-world applications: (a) watchmaking craftsmanship, and (b) belt assembly in automobile engines. ...

2020

Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problem

Maksym Andriushchenko

Classifiers used in the wild, in particular for safety-critical systems, should not only have good generalization properties but also should know when they don't know, in particular make low confidence predictions far away from the training data. We show t ...

2019

Better Algorithms for LWE and LWR

Serge Vaudenay, Alexandre Raphaël Duc, Florian Tramèr

The Learning With Error problem (LWE) is becoming more and more used in cryptography, for instance, in the design of some fully homomorphic encryption schemes. It is thus of primordial importance to find the best algorithms that might solve this problem so ...

Springer2015

Container Terminal Management

Ilaria Vacca

This thesis deals with models and methods for large scale optimization problems; in particular, we focus on decision problems arising in the context of seaport container terminals for the efficient management of terminal operations. Large-scale optimizatio ...

EPFL2011

Using reinforcement learning for optimizing the reproduction of tasks in robot programming by demonstration

Florent Günter

As robots start pervading human environments, the need for new interfaces that would simplify human-robot interaction has become more pressing. Robot Programming by Demonstration (RbD) develops intuitive ways of programming robots, taking inspiration in st ...

EPFL2009

Descriptive contour fill of partly degenerated shapes

Roger Hersch

A uniform and consistent way of filling degenerated shapes on a raster grid is presented. The processing algorithms described are based on the contour segmentation of shapes along local minima and maxima (polysegments). To reduce errors, contour segments a ...

1986

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