16 citations · 36 across the 11 of their papers we have counts for
17 papers
Back to the Manifold: Recovering from Out-of-Distribution States
Alfredo Reichlin, Giovanni Luca Marchetti, Hang Yin +2
Learning from previously collected datasets of expert data offers the promise of acquiring robotic policies without unsafe and costly online explorations. However, a major challeng…
Training and Evaluation of Deep Policies using Reinforcement Learning and Generative Models
Ali Ghadirzadeh, Petra Poklukar, Karol Arndt +4
We present a data-efficient framework for solving sequential decision-making problems which exploits the combination of reinforcement learning (RL) and latent variable generative m…
Latent-Variable Advantage-Weighted Policy Optimization for Offline RL
Xi Chen, Ali Ghadirzadeh, Tianhe Yu +6
Offline reinforcement learning methods hold the promise of learning policies from pre-collected datasets without the need to query the environment for new transitions. This setting…
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…
Meta Reinforcement Learning for Sim-to-real Domain Adaptation
Karol Arndt, Murtaza Hazara, Ali Ghadirzadeh +1
Modern reinforcement learning methods suffer from low sample efficiency and unsafe exploration, making it infeasible to train robotic policies entirely on real hardware. In this wo…
Affordance Learning for End-to-End Visuomotor Robot Control
Aleksi Hämäläinen, Karol Arndt, Ali Ghadirzadeh +1
Training end-to-end deep robot policies requires a lot of domain-, task-, and hardware-specific data, which is often costly to provide. In this work, we propose to tackle this issu…
Exploring Temporal Dependencies in Multimodal Referring Expressions with Mixed Reality
Elena Sibirtseva, Ali Ghadirzadeh, Iolanda Leite +2
In collaborative tasks, people rely both on verbal and non-verbal cues simultaneously to communicate with each other. For human-robot interaction to run smoothly and naturally, a r…
Meta-Learning for Multi-objective Reinforcement Learning
Xi Chen, Ali Ghadirzadeh, Mårten Björkman +1
Multi-objective reinforcement learning (MORL) is the generalization of standard reinforcement learning (RL) approaches to solve sequential decision making problems that consist of…
Deep Reinforcement Learning to Acquire Navigation Skills for Wheel-Legged Robots in Complex Environments
Xi Chen, Ali Ghadirzadeh, John Folkesson +1
Mobile robot navigation in complex and dynamic environments is a challenging but important problem. Reinforcement learning approaches fail to solve these tasks efficiently due to r…
Deep Predictive Policy Training using Reinforcement Learning
Ali Ghadirzadeh, Atsuto Maki, Danica Kragic +1
Skilled robot task learning is best implemented by predictive action policies due to the inherent latency of sensorimotor processes. However, training such predictive policies is c…
A Sensorimotor Reinforcement Learning Framework for Physical Human-Robot Interaction
Ali Ghadirzadeh, Judith Bütepage, Atsuto Maki +2
Modeling of physical human-robot collaborations is generally a challenging problem due to the unpredictive nature of human behavior. To address this issue, we present a data-effici…
Self-learning and adaptation in a sensorimotor framework
Ali Ghadirzadeh, Judith Bütepage, Danica Kragic +1
We present a general framework to autonomously achieve a task, where autonomy is acquired by learning sensorimotor patterns of a robot, while it is interacting with its environment…