16 citations · 17 across the 5 of their papers we have counts for
12 papers
Bayesian Meta-Learning for Few-Shot Policy Adaptation Across Robotic Platforms
Ali Ghadirzadeh, Xi Chen, Petra Poklukar +3
Reinforcement learning methods can achieve significant performance but require a large amount of training data collected on the same robotic platform. A policy trained with expensi…
Few-shot model-based adaptation in noisy conditions
Karol Arndt, Ali Ghadirzadeh, Murtaza Hazara +1
Few-shot adaptation is a challenging problem in the context of simulation-to-real transfer in robotics, requiring safe and informative data collection. In physical systems, additio…
Human-centered collaborative robots with deep reinforcement learning
Ali Ghadirzadeh, Xi Chen, Wenjie Yin +3
We present a reinforcement learning based framework for human-centered collaborative systems. The framework is proactive and balances the benefits of timely actions with the risk o…
Data-efficient visuomotor policy training using reinforcement learning and generative models
Ali Ghadirzadeh, Petra Poklukar, Ville Kyrki +2
We present a data-efficient framework for solving visuomotor sequential decision-making problems which exploits the combination of reinforcement learning (RL) and latent variable g…
Imitating by generating: deep generative models for imitation of interactive tasks
Judith Bütepage, Ali Ghadirzadeh, Özge Öztimur Karadag +2
To coordinate actions with an interaction partner requires a constant exchange of sensorimotor signals. Humans acquire these skills in infancy and early childhood mostly by imitati…
Adversarial Feature Training for Generalizable Robotic Visuomotor Control
Xi Chen, Ali Ghadirzadeh, Mårten Björkman +1
Deep reinforcement learning (RL) has enabled training action-selection policies, end-to-end, by learning a function which maps image pixels to action outputs. However, it's applica…