Name
Title | ||
|---|---|---|
Learning Crowd-Aware Robot Navigation from Challenging Environments via Distributed Deep Reinforcement Learning |
Abstract | ||
|---|---|---|
This paper presents a deep reinforcement learning (DRL) sframework for safe and efficient navigation in crowded environments. Here, the robot learns cooperative behavior using a new reward function that penalizes robot actions interfering with the pedestrian's movement. Also, we propose a simulated pedestrian policy reflecting data from actual pedestrian movements. Furthermore, we introduce a collision detection that considers the pedestrian's personal space to generate affinity robot behavior. To efficiently explore this simulation environment, we propose distributed learning using Ape-X [1]. We deployed the robot in a real environment and verified its crowd-aware navigation performance compared with an actual human in terms of path length, travel time, and the number of abrupt avoidances. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/ICRA46639.2022.9812011 | IEEE International Conference on Robotics and Automation |
DocType | Volume | Issue |
Conference | 2022 | 1 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sango Matsuzaki | 1 | 0 | 0.68 |
| Yuji Hasegawa | 2 | 0 | 0.34 |