This repository is my implementation of paper "Virtual-to-real Deep Reinforcement Learning: Continuous Control of Mobile Robots for Mapless Navigation"
I using a pioneer robot equip with SICK lidar navigative in office area ( ~10mx10m) in Coppeliasim. Agent learning to avoid collision and reaching target position.
This project have used SAC implementation from 1 and some parts from my previous projects 2 and 3 and reference some hyper-parameter settings from
4
[15/3/2024] First commit
Static environment ~ (10mx10m)
Sampling 10 among 270 sensor of SICK TIM310
State format: 10 laser range + 3 robot pose + 2 target position + 2 current twist (17)
RL agent: Soft Actor critic
Linear velocity range: (0,0.5), angular velocity range: (-1, 1), speed up v_left, v_right 4 time
TODO
Navigate in dynamic environment
CoppeliaSim simulation
The video show robot reach 2 pre-defined goal before return to initial position.
Requirements
CoppeliaSim v4.5.1 linux
ROS Noetic, rospy
Setup
Launch roscore in one terminal before launch Coppeliasim in another terminal to make sure that CoppeliaSim can load ROS plugin properly
Open vrep_scenario/room_d1.ttt in CoppeliaSim
Training using SAC python train_sac.py
Test pretrained model python test_sac.py
Note
It took near 20 hour to complete 600k step on my laptop for both simulation and training neural net, model start converge from step 300k
[5] Tai, Lei, Giuseppe Paolo, and Ming Liu. "Virtual-to-real deep reinforcement learning: Continuous control of mobile robots for mapless navigation." 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2017.
mapless-navigation-in-indoor-environments's People
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent