Generated Terrain-Robustness Benchmark (GTRB) for Legged Locomotion

A prototype to generate diverse, challenging, and realistic unstructured terrains in simulation.
With techniques of terrain authoring and active learning, the learned samplers can stably generate diverse high-quality terrains.
You may watch our video attachment for paper submission: video file, watch on youtube
This work has been accepted by ICRA 2023. preprint

For citation:

  @inproceedings{terrain_benchmark,
    title = {Generating a Terrain-Robustness Benchmark for Legged Locomotion: A Prototype via Terrain Authoring and Active Learning},
    author = {Zhang, Chong and Yang, Lizhi},
    booktitle = {2023 IEEE International Conference on Robotics and Automation (ICRA)},
    year = {2023},
  }

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Usage

Benchmark download: GTRB-v1 at this link
Main author: Chong Zhang, chozhang@ethz.ch
Non-commercial use only: CC BY-NC-SA 4.0 License

The terrains are of size (225, 225) grids.
The height values vary in [0,1], which corresponds to [0,10*gridSize].
The very initial purpose takes gridSize = 2.5cm, but it can be scaled.

The '/hard' folder in the downloaded benchmark folder is for difficult-to-traverse terrains (with high scores), and the '/medium' folder is for terrains with similar difficulties (score ~0.055).

Evaluate a policy

1. How well a base-velocity-tracking policy performs over challenging terrains? See the Tracking Challenge.
2. How well a fall-recovery policy performs over challenging terrains? See the Fall Recovery Challenge.
3. How to generate more challenging terrains? Try the tricks below!

• Add slopes. 15 degrees can make the terrains quite challenging.
• Get a larger gridSize. May rescale it to 5 cm.
• Specify varying friction coefficients. Imagine the wet stones after rain.

Importing a terrain

Import a terrain in PyBullet:

import pandas as pd

height_file = r"[your filepath]/hard/elevation0002.txt"
heightfieldData = pd.read_csv(height_file,sep=' ',header=None).values[::-1,:].flatten('C')
gridsize = .035  # rescaled from .025
terrainShape = p.createCollisionShape(shapeType=p.GEOM_HEIGHTFIELD, meshScale=[gridsize, gridsize, gridsize*10],
                                      heightfieldTextureScaling=128, heightfieldData=heightfieldData, 
                                      numHeightfieldRows=225,numHeightfieldColumns=225)
terrain = p.createMultiBody(0, terrainShape)
p.resetBasePositionAndOrientation(terrain, [0, 0, -gridsize*10], [0, 0, 0, 1])

Import a terrain in Isaac Gym:
Directly assign new height values to the subterrain height field, but requiring 2d interpolation to downsample the terrain to the typically low resolution in Isaac Gym

import pandas as pd
from scipy.interpolate import interp2d
envWidth, downsampleRes, myRes = 80, 0.1, 0.035
myCorStart, mySize = int(-112*myRes/downsampleRes), int(225*myRes/downsampleRes)
myGrids = np.linspace(-112*myRes, 112*myRes, num=225)
interpGrids = np.linspace(myCorStart*downsampleRes,(myCorStart+mySize-1)*downsampleRes, num=mySize)
algoHeightScale, myHeightScale = 0.005, myRes * 10

myMap = pd.read_csv(r'[your filepath]/hard/elevation0014.txt', sep=' ', header=None).values[::-1,:].T
myDS = interp2d(myGrids, myGrids, myMap)
interpHeight = myDS(interpGrids, interpGrids)* myHeightScale / algoHeightScale
self.height_field_raw[start_x: end_x, start_y:end_y] = 0.
self.height_field_raw[start_x + envWidth // 2 + myCorStart: start_x + envWidth // 2 + myCorStart + mySize,
            start_y + envWidth // 2 + myCorStart:start_y + envWidth // 2 + myCorStart + mySize] = interpHeight

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Repro

The repo only contains the codes for benchmark generation, and some "challenges".
Implemented on Ubuntu18.04, python3.6 in anaconda, with:

• for terrain generation: pysheds == 0.3.3, GDAL == 2.4.2, georasters == 0.5.23, tensorflow-gpu == 2.6.2, keras == 2.6.0, setuptools == 57.5.0, opencv-contrib-python == 4.6.0.66
• for flow learning: tensorflow-gpu == 2.6.2, opencv-contrib-python == 4.6.0.66, torch==1.10.2

Thanks to https://github.com/nanoxas/sketch-to-terrain for pix2pix implementation of terrain authoring!

(CC BY-NC-SA 4.0 License inherited from here)

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Todo:

1. Make the generation goal-conditioned.
2. Achieve the non-rigid terrain generation.
3. Try data-driven training with these terrains.