227 citations · 246 across the 5 of their papers we have counts for
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Hierarchically Integrated Models: Learning to Navigate from Heterogeneous Robots
Katie Kang, Gregory Kahn, Sergey Levine
Deep reinforcement learning algorithms require large and diverse datasets in order to learn successful policies for perception-based mobile navigation. However, gathering such data…
LaND: Learning to Navigate from Disengagements
Gregory Kahn, Pieter Abbeel, Sergey Levine
Consistently testing autonomous mobile robots in real world scenarios is a necessary aspect of developing autonomous navigation systems. Each time the human safety monitor disengag…
Model-Based Meta-Reinforcement Learning for Flight with Suspended Payloads
Suneel Belkhale, Rachel Li, Gregory Kahn +3
Transporting suspended payloads is challenging for autonomous aerial vehicles because the payload can cause significant and unpredictable changes to the robot's dynamics. These cha…
BADGR: An Autonomous Self-Supervised Learning-Based Navigation System
Gregory Kahn, Pieter Abbeel, Sergey Levine
Mobile robot navigation is typically regarded as a geometric problem, in which the robot's objective is to perceive the geometry of the environment in order to plan collision-free…
Composable Action-Conditioned Predictors: Flexible Off-Policy Learning for Robot Navigation
Gregory Kahn, Adam Villaflor, Pieter Abbeel +1
A general-purpose intelligent robot must be able to learn autonomously and be able to accomplish multiple tasks in order to be deployed in the real world. However, standard reinfor…