61 citations · 156 across the 12 of their papers we have counts for
14 papers · 1 filter
Understanding Collapse in Non-Contrastive Siamese Representation Learning
Alexander C. Li, Alexei A. Efros, Deepak Pathak
Contrastive methods have led a recent surge in the performance of self-supervised representation learning (SSL). Recent methods like BYOL or SimSiam purportedly distill these contr…
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…
One Policy to Control Them All: Shared Modular Policies for Agent-Agnostic Control
Wenlong Huang, Igor Mordatch, Deepak Pathak
Reinforcement learning is typically concerned with learning control policies tailored to a particular agent. We investigate whether there exists a single global policy that can gen…
Planning to Explore via Self-Supervised World Models
Ramanan Sekar, Oleh Rybkin, Kostas Daniilidis +3
Reinforcement learning allows solving complex tasks, however, the learning tends to be task-specific and the sample efficiency remains a challenge. We present Plan2Explore, a self-…