renderer

a ROS package to turn your point clouds into a simulator for training Deep Reinforcement Learning agents

(paired w/ files to interface w/ DRL agents, e.g. pytorch-rl)

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What is this?

This repo contains a ROS package that can be used to turn your point cloud (e.g. .pcd's) into a simulator, where your agent can wonder around. The /render service will render RGB or depth images from the current pose of your agent. Thus this repo can be helpful if you want to train some DRL agents (e.g. pytorch-rl) out of the point cloud data you collected, for example, of your office. This repo is developed w/ the help of @onlytailei.

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Tested system configurations:

• Ubuntu 16.04
• ROS Kinetic

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How to run:

• Compile the renderer package:

• clone the package into YOUR_ROS_WORKSPACE_DIRECTORY/src/
• catkin_make in YOUR_ROS_WORKSPACE_DIRECTORY

• Launch node:

roslaunch renderer renderer.launch

• Call service (action_ind={3(reset),0(go straight),1(turn left),2(turn right)}):

rosservice call /render action_ind

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How to pair w/ pytorch-rl?

• Turn off the rviz visualizations in ./launch/renderer.launch (cos we don't want to slow down the expensive DRL trainings :P):

• line2-3: comment off to turn off rviz
• line5: set display to false

• Copy the generated interfacing files from YOUR_ROS_WORKSPACE_DIRECTORY into pytorch-rl:

cp -r YOUR_ROS_WORKSPACE_DIRECTORY/devel/lib/python.../dist-packages/renderer YOUR_pytorch-rl_DIRECTORY/

• Copy the provided env wrapper from YOUR_ROS_WORKSPACE_DIRECTORY into pytorch-rl:

cp YOUR_ROS_WORKSPACE_DIRECTORY/envs/rendererEnv.py YOUR_pytorch-rl_DIRECTORY/core/envs/

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About the point clouds:

The pcd/office.pcd & pcd/chair.pcd are provided by my awesome colleagues Tim Caselitz (@caselitz) and Michael Krawez. If you want to use your own point clouds, you need to:

• put those .pcd's into ./data/
• modify the magic numbers in line522-527 & line549-554 in ./src/renderer.cpp to align the clouds w/ the axis
• finally rosservice call /preprocess to save the aligned clouds into ./pcd/

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The following paper might be interesting to take a look:)

VR Goggles for Robots: Real-to-sim Domain Adaptation for Visual Control: This paper deals with the reality gap from a novel perspective, targeting transferring Deep Reinforcement Learning (DRL) policies learned in simulated environments to the real-world domain for visual control tasks. Instead of adopting the common solutions to the problem by increasing the visual fidelity of synthetic images output from simulators during the training phase, this paper seeks to tackle the problem by translating the real-world image streams back to the synthetic domain during the deployment phase, to make the robot feel at home. We propose this as a lightweight, flexible, and efficient solution for visual control, as 1) no extra transfer steps are required during the expensive training of DRL agents in simulation; 2) the trained DRL agents will not be constrained to being deployable in only one specific real-world environment; 3) the policy training and the transfer operations are decoupled, and can be conducted in parallel. Besides this, we propose a conceptually simple yet very effective shift loss to constrain the consistency between subsequent frames, eliminating the need for optical flow. We validate the shift loss for artistic style transfer for videos and domain adaptation, and validate our visual control approach in real-world robot experiments. A video of our results is available at: \url{https://goo.gl/b1xz1s}. .

@article{zhang2018vr,
  title={Vr goggles for robots: Real-to-sim domain adaptation for visual control},
  author={Zhang, Jingwei and Tai, Lei and Xiong, Yufeng and Liu, Ming and Boedecker, Joschka and Burgard, Wolfram},
  journal={arXiv preprint arXiv:1802.00265},
  year={2018}
}