File size: 2,308 Bytes
3f99526 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 | """Shared, reproducible five-task corpus contract.
The paths in this corpus are intentionally heterogeneous: most tasks are packed
in HDF5 shards while LPD uses one successful rollout per seed. Never use a
parent directory name as the task label, since that would turn every LPD seed
into a separate "task". This module is the single source of truth used by all
four method trainers.
"""
from __future__ import annotations
from collections import Counter
from pathlib import Path
CANONICAL_TASKS = (
"lift_barrier",
"camera_alignment",
"three_robots_stack_cube",
"long_pipeline_delivery",
"take_photo",
)
def task_from_path(path: str) -> str:
"""Return the benchmark task without leaking an ID into the policy."""
normal = Path(path).as_posix().lower()
aliases = {
"lift_barrier": "lift_barrier",
"camera_alignment": "camera_alignment",
"three_robots_stack_cube": "three_robots_stack_cube",
"long_pipeline_delivery": "long_pipeline_delivery",
"take_photo": "take_photo",
}
for marker, task in aliases.items():
if marker in normal:
return task
raise ValueError(f"Unrecognised five-task corpus path: {path}")
def relabel_trajectories(trajectories):
"""Attach a canonical *sampling-only* task label to trajectory records."""
return [(path, key, n, present, task_from_path(path))
for path, key, n, _old_task in trajectories]
def hierarchical_item_weights(kept_trajectories, items):
"""Equalize task, episode, local arm, then time within each local arm.
The sampling label never reaches model inputs. A task gets probability
1/T; its valid demonstrations share that mass; each present local arm in a
demo shares it; and its time indices share it. This prevents 800-step LPD
streams from dominating short tasks merely because they contain more frames.
"""
episodes_per_task = Counter(task for *_prefix, task in kept_trajectories)
stream_weights = {}
for path, key, n, present, task in kept_trajectories:
weight = 1.0 / (episodes_per_task[task] * len(present) * n)
for arm in present:
stream_weights[(path, key, arm)] = weight
return [stream_weights[(path, key, arm)] for path, key, _t, arm, _task in items]
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