Publications associées à FANUC

Investigating neural resource allocation in the sensorimotor control of extra limbs

The rise of robotic body augmentation brings forth new developments that will transform robotics, human-machine interaction, and wearable electronics. Extra robotic limbs, although building upon restorative technologies, bring their own set of challenges i ...

EPFL2024

Robotic Assembly of Integrally-Attached Timber Plate Structures: From Computational Design to Automated Construction

Nicolas Henry Pierre Louis Rogeau

The digitization of timber construction, the emergence of engineered wood products, and the urgent need to drastically reduce buildings' environmental impact have given a rebirth to wood as a construction material. On the one hand, increasing the use of ti ...

EPFL2023

Geometric and Learning Methods for Robots to Navigate in Human Crowds with Application to Smart Mobility Devices

David Julian Gonon

The thesis at hand is concerned with robots' navigation in human crowds. Specifically, methods are developed for planning a mobile robot's local motion between pedestrians, and they are evaluated in experiments where a robot interacts with real pedestrians ...

EPFL2023

Reinforcement learning for scaffold-free construction of spanning-structures

Maryam Kamgarpour, Stefana Parascho, Gabriel Rémi Vallat, Anna Maria Maddux, Jingwen Wang

In construction robotics, a conventional design-to-fabrication work-flow starts with designing a structure, followed by task and robotic motion planning, and ultimately, fabrication. However, this approach can prove unsuccessful, as we may only discover th ...

New York2023

Training Efficient Controllers via Analytic Policy Gradient

Dario Floreano, Valentin Wüest, Davide Scaramuzza

Control design for robotic systems is complex and often requires solving an optimization to follow a trajectory accurately. Online optimization approaches like Model Predictive Control (MPC) have been shown to achieve great tracking performance, but requir ...

New York2023

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

Leveraging Multi-Level Modelling to Automatically Design Behavioral Arbitrators in Robotic Controllers

Alcherio Martinoli, Cyrill Silvan Baumann, Hugo Richard Birch

Automatic control design for robotic systems is becoming more and more popular. However, this usually involves a significant computational cost, due to the expensive and noisy evaluation of candidate solutions through highfidelity simulation or even real h ...

2022

Dual Stiffness Tensegrity Platform for Resilient Robotics

Dario Floreano, Stefano Mintchev, Yi Sun, Davide Zappetti

Collision resilience is an important feature of robots deployed in unstructured and partially unpredictable environments. Herein, a novel dual stiffness (DS) tensegrity platform to integrate collision resilience into a robot body is proposed. The proposed ...

2022

Model-Free Iterative Learning Control With Nonrepetitive Trajectories for Second-Order MIMO Nonlinear Systems-Application to a Delta Robot

Mohamed Bouri

A model-free iterative learning control strategy (ILC) in nonrepetitive trajectories, applied to robotic manipulators is presented in this article. The development of this ILC controller is derived in general case for second-order nonlinear multi-input mul ...

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