273 citations · 517 across the 15 of their papers we have counts for
3 papers · 1 filter
Meta-Learning Requires Meta-Augmentation
Janarthanan Rajendran, Alex Irpan, Eric Jang
Meta-learning algorithms aim to learn two components: a model that predicts targets for a task, and a base learner that quickly updates that model when given examples from a new ta…
RL-CycleGAN: Reinforcement Learning Aware Simulation-To-Real
Kanishka Rao, Chris Harris, Alex Irpan +3
Deep neural network based reinforcement learning (RL) can learn appropriate visual representations for complex tasks like vision-based robotic grasping without the need for manuall…
Scalable Multi-Task Imitation Learning with Autonomous Improvement
Avi Singh, Eric Jang, Alexander Irpan +5
While robot learning has demonstrated promising results for enabling robots to automatically acquire new skills, a critical challenge in deploying learning-based systems is scale:…