no code implementations • 4 Jul 2023 • Daniele Reda, Jungdam Won, Yuting Ye, Michiel Van de Panne, Alexander Winkler
We introduce a method to retarget motions in real-time from sparse human sensor data to characters of various morphologies.
1 code implementation • 8 May 2022 • Daniele Reda, Hung Yu Ling, Michiel Van de Panne
Key to our method is the use of a simplified model, a point mass with a virtual arm, for which we first learn a policy that can brachiate across handhold sequences with a prescribed order.
no code implementations • 11 Apr 2022 • Tianxin Tao, Daniele Reda, Michiel Van de Panne
Vision Transformers (ViT) have recently demonstrated the significant potential of transformer architectures for computer vision.
no code implementations • 22 Apr 2021 • Adam Scibior, Vasileios Lioutas, Daniele Reda, Peyman Bateni, Frank Wood
We develop a deep generative model built on a fully differentiable simulator for multi-agent trajectory prediction.
no code implementations • 9 Oct 2020 • Daniele Reda, Tianxin Tao, Michiel Van de Panne
Learning to locomote is one of the most common tasks in physics-based animation and deep reinforcement learning (RL).
no code implementations • 30 Nov 2019 • Jeffrey Hawke, Richard Shen, Corina Gurau, Siddharth Sharma, Daniele Reda, Nikolay Nikolov, Przemyslaw Mazur, Sean Micklethwaite, Nicolas Griffiths, Amar Shah, Alex Kendall
As our main contribution, we present an end-to-end conditional imitation learning approach, combining both lateral and longitudinal control on a real vehicle for following urban routes with simple traffic.
7 code implementations • 1 Jul 2018 • Alex Kendall, Jeffrey Hawke, David Janz, Przemyslaw Mazur, Daniele Reda, John-Mark Allen, Vinh-Dieu Lam, Alex Bewley, Amar Shah
We demonstrate the first application of deep reinforcement learning to autonomous driving.