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20152022
most citedHow to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learned

565 citations · 4.4k across the 119 of their papers we have counts for

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13 papers · 1 filter

cs.CV20214 cited

Pragmatic Image Compression for Human-in-the-Loop Decision-Making

Siddharth Reddy, Anca D. Dragan, Sergey Levine

Standard lossy image compression algorithms aim to preserve an image's appearance, while minimizing the number of bits needed to transmit it. However, the amount of information act…

cs.CV202129 cited

FitVid: Overfitting in Pixel-Level Video Prediction

Mohammad Babaeizadeh, Mohammad Taghi Saffar, Suraj Nair +3

An agent that is capable of predicting what happens next can perform a variety of tasks through planning with no additional training. Furthermore, such an agent can internally repr…

cs.CV2020

Inverting the Pose Forecasting Pipeline with SPF2: Sequential Pointcloud Forecasting for Sequential Pose Forecasting

Xinshuo Weng, Jianren Wang, Sergey Levine +2

Many autonomous systems forecast aspects of the future in order to aid decision-making. For example, self-driving vehicles and robotic manipulation systems often forecast future ob…

cs.CV2019

PRECOG: PREdiction Conditioned On Goals in Visual Multi-Agent Settings

Nicholas Rhinehart, Rowan McAllister, Kris Kitani +1

For autonomous vehicles (AVs) to behave appropriately on roads populated by human-driven vehicles, they must be able to reason about the uncertain intentions and decisions of other…

cs.CV2019

VideoFlow: A Conditional Flow-Based Model for Stochastic Video Generation

Manoj Kumar, Mohammad Babaeizadeh, Dumitru Erhan +4

Generative models that can model and predict sequences of future events can, in principle, learn to capture complex real-world phenomena, such as physical interactions. However, a…

cs.CV2018

Visual Memory for Robust Path Following

Ashish Kumar, Saurabh Gupta, David Fouhey +2

Humans routinely retrace paths in a novel environment both forwards and backwards despite uncertainty in their motion. This paper presents an approach for doing so. Given a demonst…