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.
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 • 11 May 2021 • Julen Urain, Anqi Li, Puze Liu, Carlo D'Eramo, Jan Peters
Reactive motion generation problems are usually solved by computing actions as a sum of policies.
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.
no code implementations • 11 Apr 2022 • Julen Urain, An T. Le, Alexander Lambert, Georgia Chalvatzaki, Byron Boots, Jan Peters
In this paper, we focus on the problem of integrating Energy-based Models (EBM) as guiding priors for motion optimization.
no code implementations • 14 Oct 2022 • Kay Hansel, Julen Urain, Jan Peters, Georgia Chalvatzaki
To combine the benefits of reactive policies and planning, we propose a hierarchical motion generation method.
1 code implementation • 8 Sep 2022 • Julen Urain, Niklas Funk, Jan Peters, Georgia Chalvatzaki
In this work, we focus on learning SE(3) diffusion models for 6DoF grasping, giving rise to a novel framework for joint grasp and motion optimization without needing to decouple grasp selection from trajectory generation.