384 citations · 1.8k across the 38 of their papers we have counts for
12 papers · 1 filter
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,…
Transformers for One-Shot Visual Imitation
Sudeep Dasari, Abhinav Gupta
Humans are able to seamlessly visually imitate others, by inferring their intentions and using past experience to achieve the same end goal. In other words, we can parse complex se…
Ask Your Humans: Using Human Instructions to Improve Generalization in Reinforcement Learning
Valerie Chen, Abhinav Gupta, Kenneth Marino
Complex, multi-task problems have proven to be difficult to solve efficiently in a sparse-reward reinforcement learning setting. In order to be sample efficient, multi-task learnin…
See, Hear, Explore: Curiosity via Audio-Visual Association
Victoria Dean, Shubham Tulsiani, Abhinav Gupta
Exploration is one of the core challenges in reinforcement learning. A common formulation of curiosity-driven exploration uses the difference between the real future and the future…
Learning Robot Skills with Temporal Variational Inference
Tanmay Shankar, Abhinav Gupta
In this paper, we address the discovery of robotic options from demonstrations in an unsupervised manner. Specifically, we present a framework to jointly learn low-level control po…