stacking_blocks_ordered/scene.yaml

schema_version: '1.0'
category: b
name: stacking_blocks_ordered
description: 'Identical scene to Category A: three blocks (red, green, blue) on the
  black mat, a magenta cross on the white mat. The instruction states the bottom-to-top
  order explicitly.'
instruction_templates:
  all_episodes: Stack the blocks from the black mat to the white mat at the cross
    position, with order from bottom to top of {c1}, {c2}, {c3} blocks.
tasks:
  1:
    episodes: 60
    order: red-green-blue
    instruction: Stack the blocks from the black mat to the white mat at the cross
      position, with order from bottom to top of red, green, blue blocks.
  2:
    episodes: 60
    order: blue-green-red
    instruction: Stack the blocks from the black mat to the white mat at the cross
      position, with order from bottom to top of blue, green, red blocks.
  3:
    episodes: 60
    order: green-red-blue
    instruction: Stack the blocks from the black mat to the white mat at the cross
      position, with order from bottom to top of green, red, blue blocks.
  4:
    episodes: 60
    order: red-blue-green
    instruction: Stack the blocks from the black mat to the white mat at the cross
      position, with order from bottom to top of red, blue, green blocks.
trained_orders:
- red-green-blue
- blue-green-red
- green-red-blue
- red-blue-green
held_out_orders:
- blue-red-green
- green-blue-red
held_out_episodes: 0
randomization:
  cross_center:
    region: centred square of side 0.1 m on the white mat
    white_mat_center:
    - 0.25
    - -0.135
    distribution: uniform
  block_xy:
    region: black mat, inset by SPAWN_MARGIN
    black_mat_center:
    - 0.25
    - 0.135
    half_extent:
    - 0.077
    - 0.067
    min_center_gap_m: 0.07
    distribution: uniform with rejection; whole set resampled if stranded
    sampled_in: canonical color order (red, green, blue, yellow)
  block_yaw_rad:
  - -0.7853981633974483
  - 0.7853981633974483
seed_formula: seed = 100_000_000*category_code + 1_000_000*task_id + episode_id +
  10_000*attempt, category_code = {a:1, b:2, c:3}; rng = numpy.random.default_rng(seed)
split_rule: episode_id % 10 == 0 -> val, else train