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Occlusion-Aware Crowd Navigation Using People as Sensors
Ye Ji Mun
, Masha Itkina
, Shuijing Liu
,
Katherine Driggs-Campbell
Electrical and Computer Engineering
Siebel School of Computing and Data Science
Coordinated Science Lab
National Center for Supercomputing Applications (NCSA)
Research output
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Chapter in Book/Report/Conference proceeding
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Conference contribution
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Keyphrases
People as Sensors
100%
Crowd Navigation
100%
Occlusion-aware
100%
Occlusion
66%
Human Agents
66%
Occlusion Inference
66%
Successful Policy
33%
Limited Sensors
33%
Loss Function
33%
Highly Dynamic
33%
Learning Representations
33%
Autonomous Navigation
33%
Inference Methods
33%
Mobile Robot
33%
Interactive Behavior
33%
Social Inference
33%
Collision Avoidance
33%
Reinforcement Learning Approach
33%
Sensor Fields
33%
Deep Reinforcement Learning (deep RL)
33%
Variational Autoencoder
33%
Policy Transfer
33%
Partially Observable Environment
33%
Turtlebot
33%
Crowded Space
33%
Computer Science
Learning Approach
100%
Mobile Robot
100%
Inference Technique
100%
Deep Reinforcement Learning
100%
Autonomous Navigation
100%
Social Inference
100%
Variational Autoencoder
100%