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ViNL: Visual Navigation and Locomotion over Obstacles

Code for our paper ViNL: Visual Navigation and Locomotion over Obstacles. Project Page

Simar Kareer*, Naoki Yokoyama*, Dhruv Batra, Sehoon Ha, Joanne Truong

What is ViNL?

Ever notice how cats can navigate while skillfully stepping over obstacles?

We were inspired to make robots do the same!

ViNL consists of:

(1) a visual navigation policy that outputs velocity commands towards a goal coordinate in unfamiliar indoor environments (trained in Habitat); and

(2) a visual locomotion policy that controls the robot’s joints to follow the velocity commands while stepping over obstacles (trained in Isaac)

Installation

Easy Version

Clone this repo and submodules git clone --recurse-submodules git@github.com:SimarKareer/ViNL.git

1. Run setup.sh. You can just copy paste the contents into your terminal. This will take around 10 minutes.
2. Locate your env and run export LD_LIBRARY_PATH=/path/to/conda/envs/vinl/lib. which python can help you locate it.
3. Test your installation by running python legged_gym/scripts/play.py --task=aliengo_nav --episode_id=-10

Hard Version

If that doesn't work here are some manual instructions

Install Isaac Gym (Instructions from legged_gym). Put it in /submodules

• Download and install Isaac Gym Preview 3 (Preview 2 will not work!) from https://developer.nvidia.com/isaac-gym
• cd isaacgym/python && pip install -e .
• Try running an example cd examples && python 1080_balls_of_solitude.py
• For troubleshooting check docs isaacgym/docs/index.html)

Install all submodules via cd submodules/isaacgym/python pip install -e . cd submodules/rsl_rl pip install -e . cd submodules/habitat-lab pip install -e .

Install requirements.txt for each submodule and main repo.

Nav Policy training code installation

Use this repo to train the navigation policy, and use this version of habitat lab

Code Structure

• root

  • The root folder is a fork of legged_gym
  • This contains all the code necessary to train locomotion policies.
  • legged_gym/envs/base/legged_robot.py: defines base legged robot tasks.
  • legged_gym/envs/aliengo/aliengo.py: defines robot and camera positions
  • legged_gym/envs/base/legged_robot_config.py: configuration for a legged robot task, inherited by other tasks like aliengo_rough_config.py or aliengo_obs_config.py
  • python legged_gym/scripts/train.py --task=<aliengo_rough | aliengo_obs>
  • python legged_gym/scripts/lbc.py: for final phase of training
  • python legged_gym/scripts/play.py --task=<aliengo_rough | aliengo_obs | aliengo_lbc | aliengo_nav>: play works for all phases of training
  • legged_gym/utils/terrain.py: defines the map, which includes spawning obstacles and walls. Wall locations are defined in resources/maps

• submodules/habitat-lab: Fork of habitat-lab. Contains code for training visual navigation policy in habitat, using photorealistic 3d scans.

• submodules/rsl_rl

  • Fork of rsl_rl.
  • Our modifications allow for Learning by Cheating style training. We use this to recreate privileged terrain information via egocentric vision.

• submodules/rsl_rl: fork of rsl_rl

Full Training Procedure

Training occurs in stages. At each stage we include the checkpoint of the previous stage to continue training.

1. Train a general purpose rough terrain walking policy (with privileged terrain info) python legged_gym/scripts/train.py --task=aliengo_rough
2. Train a obstacle orriented walking policy (with privileged terrain info) python legged_gym/scripts/train.py --task=aliengo_obs
3. Train a obstacle orriented walking policy (no privileged info terrain info). This phase of training uses vision instead of a terrain map. python legged_gym/scripts/train.py --task=aliengo_lbc
4. Train a navigation policy using this repo, and this version of habitat lab

To invidually evalauate any of these policies run python legged_gym/scripts/play.py --task=<task here>.

Evaluation

To evaluate each part of our approach 1-3 one can run python legged_gym/scripts/play.py --task=<task here>. Be sure to first edit the resume_path parameter in the approriate config first though. As an example, here's how to evaluate (1)

In aliengo_rough_config.py set resume_path="weights/rough.pt" and run python legged_gym/scripts/play.py --task=aliengo_rough

Once you have a locomotion policy (3) and navigation policy (4) you can evaluate our full approach (ViNL) with: python legged_gym/scripts/play.py --task=aliengo_nav --map=<optionally specify a floorplan> We provide a number of floorplans in resources/maps

Helpful Tips:

• You can set RECORD_FRAMES=True in legged_gym/scripts/play.py to record an episode to visually inspect. You can convert the folder of images into a video via ffmpeg -framerate 50 -pattern_type glob -i '*.png' -frames:v 9999 -c:v libx264 -pix_fmt yuv420p <videoName>.mp4

Acknowledgements

This repo is forked from legged gym. We rely on rsl_rl for rl algorithm implementation and isaacgym as our simulator. For information about how to add new tasks, robots, etc see legged_gym

Citation

@inproceedings{kareer2022vinl,
    title={ViNL: Visual Navigation and Locomotion Over Obstacles}, 
    author={Simar Kareer and Naoki Yokoyama and Dhruv Batra and Sehoon Ha and Joanne Truong}, 
    booktitle={International Conference on Robotics and Automation (ICRA)},
    year={2023}
}