Ignacio Carlucho
Orcid: 0000-0002-6262-480X
According to our database1,
Ignacio Carlucho
authored at least 29 papers
between 2017 and 2024.
Collaborative distances:
Collaborative distances:
Timeline
Legend:
Book In proceedings Article PhD thesis Dataset OtherLinks
Online presence:
-
on orcid.org
On csauthors.net:
Bibliography
2024
SpasticMyoElbow: Physical Human-Robot Interaction Simulation Framework for Modelling Elbow Spasticity.
CoRR, 2024
MarineGym: Accelerated Training for Underwater Vehicles with High-Fidelity RL Simulation.
CoRR, 2024
Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2024
Replication of Impedance Identification Experiments on a Reinforcement-Learning-Controlled Digital Twin of Human Elbows.
Proceedings of the International Joint Conference on Neural Networks, 2024
Proceedings of the European Robotics Forum 2024, 2024
Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems, 2024
2023
Generating Teammates for Training Robust Ad Hoc Teamwork Agents via Best-Response Diversity.
Trans. Mach. Learn. Res., 2023
A General Learning Framework for Open Ad Hoc Teamwork Using Graph-based Policy Learning.
J. Mach. Learn. Res., 2023
Semi-Parametric Control Architecture for Autonomous Underwater Vehicles Subject to Time Delays.
IEEE Access, 2023
Proceedings of the First Tiny Papers Track at ICLR 2023, 2023
2022
CoRR, 2022
MIDGARD: A Simulation Platform for Autonomous Navigation in Unstructured Environments.
CoRR, 2022
Proceedings of the 2022 International Conference on Robotics and Automation, 2022
Proceedings of the Multi-Agent Systems - 19th European Conference, 2022
2021
Data-driven controllers and the need for perception systems in underwater manipulation.
CoRR, 2021
2020
A reinforcement learning control approach for underwater manipulation under position and torque constraints.
CoRR, 2020
Deep reinforcement learning approach for MPPT control of partially shaded PV systems in Smart Grids.
Appl. Soft Comput., 2020
2019
2018
Adaptive low-level control of autonomous underwater vehicles using deep reinforcement learning.
Robotics Auton. Syst., 2018
2017
Expert Syst. Appl., 2017