8 citations · 9 across the 4 of their papers we have counts for
6 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…
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…
Flexible Disaster Response of Tomorrow -- Final Presentation and Evaluation of the CENTAURO System
Tobias Klamt, Diego Rodriguez, Lorenzo Baccelliere +29
Mobile manipulation robots have high potential to support rescue forces in disaster-response missions. Despite the difficulties imposed by real-world scenarios, robots are promisin…
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-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…