Reinforcement learning models trained only in simulation using automatic domain randomization solve Rubik's cube with a real robot hand.
Unsupervised meta- learning for reinforcement learning.arXiv preprint arXiv:1806.04640
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 2roles
background 1polarities
background 1representative citing papers
SISL adds self-improving decoupled policies and return-based prioritization to skill-based meta-RL to achieve stable adaptation from noisy demonstrations on long-horizon tasks.
citing papers explorer
-
Solving Rubik's Cube with a Robot Hand
Reinforcement learning models trained only in simulation using automatic domain randomization solve Rubik's cube with a real robot hand.
-
Self-Improving Skill Learning for Robust Skill-based Meta-Reinforcement Learning
SISL adds self-improving decoupled policies and return-based prioritization to skill-based meta-RL to achieve stable adaptation from noisy demonstrations on long-horizon tasks.