activity
20152022
most citedRobust Adversarial Reinforcement Learning

384 citations · 1.8k across the 38 of their papers we have counts for

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Showing 2020Show all

23 papers · 1 filter

cs.CV2020

Audio-Visual Floorplan Reconstruction

Senthil Purushwalkam, Sebastian Vicenc Amengual Gari, Vamsi Krishna Ithapu +4

Given only a few glimpses of an environment, how much can we infer about its entire floorplan? Existing methods can map only what is visible or immediately apparent from context, a…

cs.CV20205 cited

KRISP: Integrating Implicit and Symbolic Knowledge for Open-Domain Knowledge-Based VQA

Kenneth Marino, Xinlei Chen, Devi Parikh +2

One of the most challenging question types in VQA is when answering the question requires outside knowledge not present in the image. In this work we study open-domain knowledge, t…

cs.LG20209 cited

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,…

cs.RO202018 cited

Same Object, Different Grasps: Data and Semantic Knowledge for Task-Oriented Grasping

Adithyavairavan Murali, Weiyu Liu, Kenneth Marino +2

Despite the enormous progress and generalization in robotic grasping in recent years, existing methods have yet to scale and generalize task-oriented grasping to the same extent. T…

cs.LG20207 cited

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…

cs.LG2020

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…