307 citations · 509 across the 20 of their papers we have counts for
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Efficient RL via Disentangled Environment and Agent Representations
Kevin Gmelin, Shikhar Bahl, Russell Mendonca +1
Agents that are aware of the separation between themselves and their environments can leverage this understanding to form effective representations of visual input. We propose an a…
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…
Neural Dynamic Policies for End-to-End Sensorimotor Learning
Shikhar Bahl, Mustafa Mukadam, Abhinav Gupta +1
The current dominant paradigm in sensorimotor control, whether imitation or reinforcement learning, is to train policies directly in raw action spaces such as torque, joint angle,…
Skew-Fit: State-Covering Self-Supervised Reinforcement Learning
Vitchyr H. Pong, Murtaza Dalal, Steven Lin +3
Autonomous agents that must exhibit flexible and broad capabilities will need to be equipped with large repertoires of skills. Defining each skill with a manually-designed reward f…
Visual Reinforcement Learning with Imagined Goals
Ashvin Nair, Vitchyr Pong, Murtaza Dalal +3
For an autonomous agent to fulfill a wide range of user-specified goals at test time, it must be able to learn broadly applicable and general-purpose skill repertoires. Furthermore…