research
∙
05/12/2023
Beyond invariant representation learning: linearly alignable latent spaces for efficient closed-form domain adaptation
Optimal transport (OT) is a powerful geometric tool used to compare and ...
share
research
∙
09/02/2022
Co-Imitation: Learning Design and Behaviour by Imitation
The co-adaptation of robots has been a long-standing research endeavour ...
research
∙
06/29/2022
Online vs. Offline Adaptive Domain Randomization Benchmark
Physics simulators have shown great promise for conveniently learning re...
research
∙
04/18/2022
Training and Evaluation of Deep Policies using Reinforcement Learning and Generative Models
We present a data-efficient framework for solving sequential decision-ma...
research
∙
01/27/2022
SafeAPT: Safe Simulation-to-Real Robot Learning using Diverse Policies Learned in Simulation
The framework of Simulation-to-real learning, i.e, learning policies in ...
research
∙
01/20/2022
DROPO: Sim-to-Real Transfer with Offline Domain Randomization
In recent years, domain randomization has gained a lot of traction as a ...
research
∙
05/25/2021
Affine Transport for Sim-to-Real Domain Adaptation
Sample-efficient domain adaptation is an open problem in robotics. In th...
research
∙
03/12/2021
Domain Curiosity: Learning Efficient Data Collection Strategies for Domain Adaptation
Domain adaptation is a common problem in robotics, with applications suc...
research
∙
10/16/2020
Few-shot model-based adaptation in noisy conditions
Few-shot adaptation is a challenging problem in the context of simulatio...
research
∙
09/16/2019
Meta Reinforcement Learning for Sim-to-real Domain Adaptation
Modern reinforcement learning methods suffer from low sample efficiency ...
research
∙
03/10/2019
