Publications related to Noise-Resistant Particle Swarm Optimization for the Learning of Robust Obstacle Avoidance Controllers using a Depth Camera

C-shells: Deployable Gridshells with Curved Beams

Mark Pauly, Francis Julian Panetta, Seiichi Eduardo Suzuki Erazo, Quentin Christian Becker, Yingying Ren, Davide Pellis

We introduce a computational pipeline for simulating and designing C-shells, a new class of planar-to-spatial deployable linkage structures. A C-shell is composed of curved flexible beams connected at rotational joints that can be assembled in a stress-fre ...

2023

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

A Noise-Resistant Mixed-Discrete Particle Swarm Optimization Algorithm for the Automatic Design of Robotic Controllers

Alcherio Martinoli, Cyrill Silvan Baumann

The automatic design of well-performing robotic controllers is still an unsolved problem due to the inherently large parameter space and noisy, often hard-to-define performance metrics, especially when sequential tasks need to be accomplished. Distal contr ...

2022

Pareto-like sequential sampling heuristic for global optimisation

Katrin Beyer, Mahmoud S. M. Shaqfa

In this paper, we propose a simple global optimisation algorithm inspired by Pareto’s principle. This algorithm samples most of its solutions within prominent search domains and is equipped with a self-adaptive mechanism to control the dynamic tightening o ...

2021

Reality-assisted evolution of soft robots through large-scale physical experimentation: a review

Josephine Anna Eleanor Hughes, Simon Lukas Hauser

We introduce the framework of reality-assisted evolution to summarize a growing trend towards combining model-based and model-free approaches to improve the design of physically embodied soft robots. In silico, data-driven models build, adapt, and improve ...

MIT Press Journals2021

Convergence of adaptive algorithms for constrained weakly convex optimization

Volkan Cevher, Ahmet Alacaoglu, Yurii Malitskyi

We analyze the adaptive first order algorithm AMSGrad, for solving a constrained stochastic optimization problem with a weakly convex objective. We prove the

\tilde O(t^{-1/2})

rate of convergence for the squared norm of the gradient of Moreau envelope, ...

2021

An optimized material removal process

Jean-François Molinari, Son-Jonathan Pham-Ba

We conduct boundary element simulations of a contact problem consisting of an elastic medium subject to tangential load. Using a particle swarm optimization algorithm, we find the optimal shape and location of the micro-contacts to maximize for a given loa ...

2020

SwarmLab: a MATLAB Drone Swarm Simulator

Dario Floreano, Fabrizio Schiano, Enrica Soria

Among the available solutions for drone swarm simulations, we identified a lack of simulation frameworks that allow easy algorithms prototyping, tuning, debugging and performance analysis. Moreover, users who want to dive in the research field of drone swa ...

2020

From human action understanding to robot action execution: how the physical properties of handled objects modulate non-verbal cues

Aude Billard, Konstantinos Chatzilygeroudis, Nuno Ricardo Ferreira Duarte, José Santos-Victor

Humans manage to communicate action intentions in a non-verbal way, through body posture and movement. We start from this observation to investigate how a robot can decode a human's non-verbal cues during the manipulation of an object, with specific physic ...

IEEE2020

Low-Complexity Optimization-Based Control: Design, Methods and Applications

Ivan Pejcic

Optimization-based controllers are advanced control systems whose mechanism of determining control inputs requires the solution of a mathematical optimization problem. In this thesis, several contributions related to the computational effort required for o ...

EPFL2019

Noise-Resistant Particle Swarm Optimization for the Learning of Robust Obstacle Avoidance Controllers using a Depth Camera

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C-shells: Deployable Gridshells with Curved Beams

Memory of Motion for Initializing Optimization in Robotics

A Noise-Resistant Mixed-Discrete Particle Swarm Optimization Algorithm for the Automatic Design of Robotic Controllers

Pareto-like sequential sampling heuristic for global optimisation

Reality-assisted evolution of soft robots through large-scale physical experimentation: a review

Convergence of adaptive algorithms for constrained weakly convex optimization

An optimized material removal process

SwarmLab: a MATLAB Drone Swarm Simulator

From human action understanding to robot action execution: how the physical properties of handled objects modulate non-verbal cues

Low-Complexity Optimization-Based Control: Design, Methods and Applications

C-shells: Deployable Gridshells with Curved Beams

Memory of Motion for Initializing Optimization in Robotics

Pareto-like sequential sampling heuristic for global optimisation

SwarmLab: a MATLAB Drone Swarm Simulator

Low-Complexity Optimization-Based Control: Design, Methods and Applications

An optimized material removal process

Convergence of adaptive algorithms for constrained weakly convex optimization

Reality-assisted evolution of soft robots through large-scale physical experimentation: a review

A Noise-Resistant Mixed-Discrete Particle Swarm Optimization Algorithm for the Automatic Design of Robotic Controllers

From human action understanding to robot action execution: how the physical properties of handled objects modulate non-verbal cues