240 citations · 283 across the 3 of their papers we have counts for
4 papers
Collision Avoidance in Pedestrian-Rich Environments with Deep Reinforcement Learning
Michael Everett, Yu Fan Chen, Jonathan P. How
Collision avoidance algorithms are essential for safe and efficient robot operation among pedestrians. This work proposes using deep reinforcement (RL) learning as a framework to m…
Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning
Michael Everett, Yu Fan Chen, Jonathan P. How
Robots that navigate among pedestrians use collision avoidance algorithms to enable safe and efficient operation. Recent works present deep reinforcement learning as a framework to…
Socially Aware Motion Planning with Deep Reinforcement Learning
Yu Fan Chen, Michael Everett, Miao Liu +1
For robotic vehicles to navigate safely and efficiently in pedestrian-rich environments, it is important to model subtle human behaviors and navigation rules (e.g., passing on the…
Decentralized Non-communicating Multiagent Collision Avoidance with Deep Reinforcement Learning
Yu Fan Chen, Miao Liu, Michael Everett +1
Finding feasible, collision-free paths for multiagent systems can be challenging, particularly in non-communicating scenarios where each agent's intent (e.g. goal) is unobservable…