101 citations · 339 across the 13 of their papers we have counts for
3 papers · 1 filter
Deep Dynamics Models for Learning Dexterous Manipulation
Anusha Nagabandi, Kurt Konoglie, Sergey Levine +1
Dexterous multi-fingered hands can provide robots with the ability to flexibly perform a wide range of manipulation skills. However, many of the more complex behaviors are also not…
Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model
Alex X. Lee, Anusha Nagabandi, Pieter Abbeel +1
Deep reinforcement learning (RL) algorithms can use high-capacity deep networks to learn directly from image observations. However, these high-dimensional observation spaces presen…
Deep Online Learning via Meta-Learning: Continual Adaptation for Model-Based RL
Anusha Nagabandi, Chelsea Finn, Sergey Levine
Humans and animals can learn complex predictive models that allow them to accurately and reliably reason about real-world phenomena, and they can adapt such models extremely quickl…