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perception-terrain-randomization

perception-terrain-randomization

Configure mjlab sensors, terrain generation, flat patches, and domain randomization with CPU/CUDA verification boundaries.

Independent GitHub snapshot · Automated classification · Original documentation governs

Documentation de la Skill

GitHub

name: perception-terrain-randomization description: "Configure mjlab sensors, terrain generation, flat patches, and domain randomization with CPU/CUDA verification boundaries." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

Perception, Terrain, and Randomization

Use this sub-skill when an mjlab task needs perception sensors, terrain, terrain-aware resets/curriculum, or runtime domain randomization.

Route here for

  • Scene-level BuiltinSensorCfg, ContactSensorCfg, RayCastSensorCfg, TerrainHeightSensorCfg, and CameraSensorCfg selection.
  • Sensor data semantics: contact reductions/history, raycast hit distances, terrain height scans, RGB/depth/segmentation outputs, and debug visualization.
  • TerrainEntityCfg, TerrainGeneratorCfg, terrain presets, curriculum grid behavior, spawn proportions, and flat patch sampling.
  • Domain-randomization event functions in mjlab.envs.mdp.dr, including model field expansion, recomputation levels, camera/render implications, and visualization limits.
  • Deciding whether a check is CPU-config-only or a CUDA/full sensor validation.

Route elsewhere

Operating workflow

  1. Identify which layer owns the request: sensor config, terrain config, terrain-aware reset/curriculum, or dr event term.
  2. Confirm exact installed signatures and terrain preset names with the bundled inspection script when the mjlab version may matter. Use --json for machine-readable output.
  3. Read the relevant reference:
    • Sensors for built-in/contact/raycast/camera data.
    • Terrain for generator modes, presets, flat patches, curriculum, and debug groups.
    • Domain randomization for event modes, field selection, operations/distributions, and recompute behavior.
    • Troubleshooting for common sensor, terrain, rendering, and DR failures.
  4. Choose verification level explicitly:
    • CPU/config checks can inspect imports, dataclass signatures, CLI/help text, and pure terrain config construction.
    • CUDA/full checks are needed for raycast/camera sensing, MuJoCo Warp render context behavior, sensor-context creation, and training-scale simulation.

Fast decisions

  • Use ContactSensorCfg for structured contact data and gait air-time signals.
  • Use RayCastSensorCfg with a GridPatternCfg for terrain scans and with a PinholeCameraPatternCfg for depth-like rays.
  • Use TerrainHeightSensorCfg when the policy needs per-frame vertical clearance rather than raw raycast hit positions.
  • Use CameraSensorCfg for RGB/depth/segmentation images; keep all camera render settings identical inside one scene.
  • Use terrain named configs for standard locomotion terrain, then customize with dataclasses.replace() or by replacing sub_terrains.
  • Prefer startup or reset events for DR functions that trigger model recomputation; reserve frequent interval/step events for cheap state or friction-style changes.
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