61 citations · 156 across the 12 of their papers we have counts for
4 papers · 1 filter
Accelerating Robotic Reinforcement Learning via Parameterized Action Primitives
Murtaza Dalal, Deepak Pathak, Ruslan Salakhutdinov
Despite the potential of reinforcement learning (RL) for building general-purpose robotic systems, training RL agents to solve robotics tasks still remains challenging due to the d…
Hierarchical Neural Dynamic Policies
Shikhar Bahl, Abhinav Gupta, Deepak Pathak
We tackle the problem of generalization to unseen configurations for dynamic tasks in the real world while learning from high-dimensional image input. The family of nonlinear dynam…
Unsupervised Learning of Visual 3D Keypoints for Control
Boyuan Chen, Pieter Abbeel, Deepak Pathak
Learning sensorimotor control policies from high-dimensional images crucially relies on the quality of the underlying visual representations. Prior works show that structured laten…
Auto-Tuned Sim-to-Real Transfer
Yuqing Du, Olivia Watkins, Trevor Darrell +2
Policies trained in simulation often fail when transferred to the real world due to the `reality gap' where the simulator is unable to accurately capture the dynamics and visual pr…