Environment Spec Examples

Each example is a complete environment spec: a human MSK model, an assistive device, and a terrain. Both the Controller Optimization and Reinforcement Learning pipelines can use it. See Defining an Environment for the field reference. These specs also ship in the docs/examples/ folder of the myoassist repo.

Example	MSK	Device	Terrain
Exoskeleton, flat	myolegs22	Humotech_L1	flat
Exoskeleton, slope	myolegs22	Humotech_L1	8° slope
Prosthesis, rough	myolegs22	OpenSourceLeg_A_L1	rough heightfield (6 cm)
Tiled course	myolegs22	Humotech_L1	flat, slope, and stairs
Random tiled grid	myolegs22	OpenExo_L1	randomized 3×3 grid

Exoskeleton on flat ground

{ "msk": "myolegs22", "device": "Humotech_L1" }

Exoskeleton on an 8° slope

{
  "msk": "myolegs22",
  "device": "Humotech_L1",
  "terrain": { "terrain": "slope", "deg": 8 }
}

Prosthesis on rough ground

{
  "msk": "myolegs22",
  "device": "OpenSourceLeg_A_L1",
  "terrain": { "terrain": "random", "amplitude": 0.06 }
}

Tiled course (flat, slope, stairs)

{
  "msk": "myolegs22",
  "device": "Humotech_L1",
  "terrain": {
    "terrain_name": "mixed_course",
    "grid": { "rows": 1, "cols": 3, "tile_size": [4.0, 4.0] },
    "border": { "width": 0.5, "match_mode": "min" },
    "palette_preset": "uniform",
    "tiles": [
      { "row": 0, "col": 0, "type": "flat",   "params": { "height": 0.0 } },
      { "row": 0, "col": 1, "type": "slope",  "params": { "angle_deg": 8.0, "axis": "x", "plateau_ratio": 0.1 } },
      { "row": 0, "col": 2, "type": "stairs", "params": { "n_steps": 5, "step_height": 0.1, "peak_width": 0.4, "axis": "x" } }
    ]
  }
}

Randomized 3×3 tiled grid

{
  "msk": "myolegs22",
  "device": "OpenExo_L1",
  "terrain": {
    "terrain_name": "random_course_3x3",
    "grid": { "rows": 3, "cols": 3, "tile_size": [8.0, 8.0] },
    "border": { "width": 0.5, "match_mode": "min" },
    "palette_preset": "diverse",
    "tiles": [
      { "row": 1, "col": 1, "type": "flat", "params": { "height": 0.0 } }
    ],
    "randomization": {
      "seed": 17,
      "weights": { "rough": 0.4, "stairs": 0.2, "slope": 0.2, "stepping_stones": 0.2 }
    }
  }
}

Using an example

# Controller Optimization (reflex)
python -m ctrl_optim.optim.train --env-spec docs/examples/env_exo_slope.json --sim_time 20 -eff --ExoOn 1 ...

# Programmatically
from myoassist_utils.env_spec import EnvSpec

spec = EnvSpec.load("docs/examples/env_exo_slope.json").validate()
xml = spec.compose()   # returns a loadable MJCF string

To make your own, copy an example and change the keys. Run python -m assist_sim list for every valid MSK and device, and see Defining an Environment for the terrain field.