no code implementations • 13 Apr 2024 • Puze Liu, Haitham Bou-Ammar, Jan Peters, Davide Tateo
Videos of the real robot experiments are available on the project website (https://puzeliu. github. io/TRO-ATACOM).
1 code implementation • ICLR 2020 • Carlo D'Eramo, Davide Tateo, Andrea Bonarini, Marcello Restelli, Jan Peters
We study the benefit of sharing representations among tasks to enable the effective use of deep neural networks in Multi-Task Reinforcement Learning.
no code implementations • 13 Nov 2023 • Luca Lach, Robert Haschke, Davide Tateo, Jan Peters, Helge Ritter, Júlia Borràs, Carme Torras
The advent of tactile sensors in robotics has sparked many ideas on how robots can leverage direct contact measurements of their environment interactions to improve manipulation tasks.
no code implementations • 7 Nov 2023 • Firas Al-Hafez, Guoping Zhao, Jan Peters, Davide Tateo
Stateful policies play an important role in reinforcement learning, such as handling partially observable environments, enhancing robustness, or imposing an inductive bias directly into the policy structure.
2 code implementations • 4 Nov 2023 • Firas Al-Hafez, Guoping Zhao, Jan Peters, Davide Tateo
Imitation Learning (IL) holds great promise for enabling agile locomotion in embodied agents.
1 code implementation • 1 Mar 2023 • Firas Al-Hafez, Davide Tateo, Oleg Arenz, Guoping Zhao, Jan Peters
Recent methods for imitation learning directly learn a $Q$-function using an implicit reward formulation rather than an explicit reward function.
1 code implementation • 11 Jan 2023 • Piotr Kicki, Puze Liu, Davide Tateo, Haitham Bou-Ammar, Krzysztof Walas, Piotr Skrzypczyński, Jan Peters
Motion planning is a mature area of research in robotics with many well-established methods based on optimization or sampling the state space, suitable for solving kinematic motion planning.
no code implementations • 27 Sep 2022 • Puze Liu, Kuo Zhang, Davide Tateo, Snehal Jauhri, Zhiyuan Hu, Jan Peters, Georgia Chalvatzaki
Our proposed approach achieves state-of-the-art performance in simulated high-dimensional and dynamic tasks while avoiding collisions with the environment.
1 code implementation • 21 Jun 2022 • Davide Tateo, Davide Antonio Cucci, Matteo Matteucci, Andrea Bonarini
In this paper, we propose the use of an efficient representation, based on structural points, for the geometry of objects to be used as landmarks in a monocular semantic SLAM system based on the pose-graph formulation.
no code implementations • 9 Mar 2022 • Marius Memmel, Puze Liu, Davide Tateo, Jan Peters
Black-box policy optimization is a class of reinforcement learning algorithms that explores and updates the policies at the parameter level.
no code implementations • 9 Mar 2022 • Puze Liu, Kuo Zhang, Davide Tateo, Snehal Jauhri, Jan Peters, Georgia Chalvatzaki
Autonomous robots should operate in real-world dynamic environments and collaborate with humans in tight spaces.
no code implementations • 22 Oct 2021 • Julen Urain, Davide Tateo, Jan Peters
Learning robot motions from demonstration requires models able to specify vector fields for the full robot pose when the task is defined in operational space.
1 code implementation • 20 Jul 2021 • João Carvalho, Davide Tateo, Fabio Muratore, Jan Peters
This estimator is unbiased, has low variance, and can be used with differentiable and non-differentiable function approximators.
no code implementations • 11 Dec 2020 • Julen Urain, Davide Tateo, Tianyu Ren, Jan Peters
We present a new family of deep neural network-based dynamic systems.
no code implementations • 25 Oct 2020 • Julen Urain, Michelle Ginesi, Davide Tateo, Jan Peters
We introduce ImitationFlow, a novel Deep generative model that allows learning complex globally stable, stochastic, nonlinear dynamics.
1 code implementation • 10 Jun 2020 • Riad Akrour, Davide Tateo, Jan Peters
Reinforcement learning (RL) has demonstrated its ability to solve high dimensional tasks by leveraging non-linear function approximators.
2 code implementations • 4 Jan 2020 • Carlo D'Eramo, Davide Tateo, Andrea Bonarini, Marcello Restelli, Jan Peters
MushroomRL is an open-source Python library developed to simplify the process of implementing and running Reinforcement Learning (RL) experiments.
1 code implementation • 1 Jan 2020 • Simone Parisi, Davide Tateo, Maximilian Hensel, Carlo D'Eramo, Jan Peters, Joni Pajarinen
Empirical results on classic and novel benchmarks show that the proposed approach outperforms existing methods in environments with sparse rewards, especially in the presence of rewards that create suboptimal modes of the objective function.