Robot learning from randomized simulations: A review
The rise of deep learning has caused a paradigm shift in robotics research, favoring
methods that require large amounts of data. Unfortunately, it is prohibitively expensive to …
methods that require large amounts of data. Unfortunately, it is prohibitively expensive to …
Cyclic policy distillation: Sample-efficient sim-to-real reinforcement learning with domain randomization
Deep reinforcement learning with domain randomization learns a control policy in various
simulations with randomized physical and sensor model parameters to become transferable …
simulations with randomized physical and sensor model parameters to become transferable …
TWIST: Teacher-Student World Model Distillation for Efficient Sim-to-Real Transfer
Model-based RL is a promising approach for real-world robotics due to its improved sample
efficiency and generalization capabilities compared to model-free RL. However, effective …
efficiency and generalization capabilities compared to model-free RL. However, effective …
Wh-AI-les: Exploring harmonized vision models robustness against distribution shift
M Mounsif, M Benabdelkrim… - 2023 IEEE 13th …, 2023 - ieeexplore.ieee.org
The remarkable and increasing efficiency of learning-based vision strategies has induced
strong paradigm shift in favor of neural architectures that are consequently finding their way …
strong paradigm shift in favor of neural architectures that are consequently finding their way …