Files
lerobot-clone/scripts/ci/extract_task_descriptions.py
Pepijn 282c31cfef feat(envs): add RoboMME benchmark (#3311)
* feat(envs): add RoboMME benchmark integration

- RoboMME env wrapper with image/wrist_image/state observations
- Docker image with Vulkan, SAPIEN, mani-skill deps
- CI workflow: 1-episode smoke eval with pepijn223/smolvla_robomme
- preprocess_observation: handle image/wrist_image/state keys
- pyproject.toml: robomme extra

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* refactor(docker): rebase RoboMME image on huggingface/lerobot-gpu

Mirror the libero/metaworld pattern: start from the nightly GPU image
(which already has apt deps, uv, venv, and lerobot[all] preinstalled)
and only layer on what RoboMME uniquely needs — the Vulkan libs
ManiSkill/SAPIEN requires, plus the robomme extra with the
gymnasium/numpy overrides.

Drops 48 lines of duplicated base setup (CUDA FROM, python install,
user creation, venv init, base apt deps) that the nightly image already
provides. Net: 102 → 54 lines.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* docs(robomme): drop prototype-branch note and move dataset to lerobot/robomme

- Remove the "Related work" block referencing the prototype branch
  feat/robomme-integration; the PR stands on its own.
- Point all dataset references at lerobot/robomme (docs, env module
  docstring, RoboMMEEnvConfig docstring) — this is the canonical HF
  location once the dataset is mirrored.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix(robomme): make docs build + fast tests green

1. Docs: add robomme to _toctree.yml under Benchmarks so doc-builder's
   TOC integrity check stops rejecting the new page.

2. Fast tests: robomme's mani-skill transitively pins numpy<2 which is
   unsatisfiable against the project's numpy>=2 base pin, so `uv sync`
   couldn't resolve a universal lockfile.

   Drop robomme as a pyproject extra entirely — it truly cannot coexist
   with the rest of the dep tree. The Dockerfile installs robomme
   directly from its git URL via `uv pip install --override`, which was
   already the runtime path. pyproject, docs, env docstrings, and the
   CI job comment all now point to the docker-only install.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* test(robomme): realign unit tests with current env API

The tests were written against an earlier env layout and never updated when
the wrapper was refactored, so CI's fast-test job was failing with:

- KeyError: 'front_rgb' / 'wrist_rgb' — these were renamed to the
  lerobot-canonical 'image' / 'wrist_image' keys (matching the dataset
  columns and preprocess_observation's built-in fallbacks).
- AssertionError: 'robomme' not in result — create_robomme_envs now
  returns {task_name: {task_id: env}}, not {'robomme': {...}}, so
  comma-separated task lists work.
- ModuleNotFoundError: lerobot.envs.lazy_vec_env — LazyVectorEnv was
  removed; create_robomme_envs is straightforward synchronous now.

Rewrite the 7 failing cases against the current API, drop the three
LazyVectorEnv tests, and add a multi-task test so the new comma-separated
task parsing is covered. Stub install/teardown is moved into helpers
(`_install_robomme_stub` / `_uninstall_robomme_stub`) so individual tests
stop repeating six boilerplate lines.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* ci: point benchmark eval checkpoints at the lerobot/ org mirrors

pepijn223/smolvla_* → lerobot/smolvla_* across every benchmark job in
this branch (libero, metaworld, and the per-branch benchmark). The
checkpoints were mirrored into the lerobot/ org and that's the canonical
location going forward.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix: integrate PR #3311 review feedback

- envs: rename obs keys to pixels/image, pixels/wrist_image, agent_pos
- envs: add __post_init__ for dynamic action_dim in RoboMMEEnv config
- envs: remove special-case obs conversion in utils.py (no longer needed)
- ci: add Docker Hub login, HF_USER_TOKEN guard, --env.task_ids=[0]
- scripts: extract_task_descriptions supports multiple task_ids
- docs: title to # RoboMME, add image, restructure eval section
- tests: update all key assertions to match new obs naming

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix(docs): use correct RoboMME teaser image URL

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* ci(robomme): smoke-eval 10 tasks instead of 5

Broader coverage on the RoboMME benchmark CI job: bump the smoke eval
from 5 tasks to 10 (one episode each), all drawn from ROBOMME_TASKS.

Tasks now run: PickXtimes, BinFill, StopCube, MoveCube, InsertPeg,
SwingXtimes, VideoUnmask, ButtonUnmask, PickHighlight, PatternLock.

Updated the parse_eval_metrics.py `--task` label from the single
`PickXtimes` stub to the full comma list so the metrics artifact
reflects what was actually run. `parse_eval_metrics.py` already reads
`overall` for multi-task runs, so no parser change is needed.

Made-with: Cursor

* fix(robomme): nest `pixels` as a dict so preprocess_observation picks it up

`_convert_obs` was returning flat keys (`pixels/image`,
`pixels/wrist_image`). `preprocess_observation()` in envs/utils.py
keys off the top-level `"pixels"` entry and, not finding it,
silently dropped every image from the batch. The policy then saw
zero image features and raised

    ValueError: All image features are missing from the batch.

Match the LIBERO layout: return
`{"pixels": {"image": ..., "wrist_image": ...}, "agent_pos": ...}`
and declare the same shape in `observation_space`.

Made-with: Cursor

* fix(robomme): align docs and tests with nested pixels obs layout

Addresses PR #3311 review feedback:
- Docs: correct observation keys to `pixels/image` / `pixels/wrist_image`
  (mapped to `observation.images.image` / `observation.images.wrist_image`)
  and drop the now-obsolete column-rename snippet.
- Tests: assert `result["pixels"]["image"]` instead of flat `pixels/image`,
  matching the nested layout required by `preprocess_observation()`.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

* fix(envs): preserve AsyncVectorEnv metadata/unwrapped in lazy eval envs

Port of #3416 onto this branch.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

* ci: gate Docker Hub login on secret availability

Fork PRs cannot access `secrets.DOCKERHUB_LEROBOT_{USERNAME,PASSWORD}`,
which made every benchmark job fail at the login step. Gate the login
on the env-var expansion of the username so the step is skipped (not
failed) when secrets are absent.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

* fix(robomme): address review feedback

---------

Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-20 20:21:27 +02:00

174 lines
7.0 KiB
Python

#!/usr/bin/env python3
# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Extract natural-language task descriptions for a benchmark suite.
Runs inside the benchmark Docker container (where the env library is installed)
immediately after lerobot-eval, writing a JSON file that parse_eval_metrics.py
picks up and embeds in metrics.json.
Output format: {"<suite>_<task_idx>": "<nl instruction>", ...}
Usage:
python scripts/ci/extract_task_descriptions.py \\
--env libero --task libero_spatial \\
--output /tmp/eval-artifacts/task_descriptions.json
"""
from __future__ import annotations
import argparse
import json
import sys
from pathlib import Path
def _libero_descriptions(task_suite: str) -> dict[str, str]:
from libero.libero import benchmark # type: ignore[import-untyped]
suite_dict = benchmark.get_benchmark_dict()
if task_suite not in suite_dict:
print(
f"[extract_task_descriptions] Unknown LIBERO suite '{task_suite}'. "
f"Available: {list(suite_dict.keys())}",
file=sys.stderr,
)
return {}
suite = suite_dict[task_suite]()
return {f"{task_suite}_{i}": suite.get_task(i).language for i in range(suite.n_tasks)}
def _metaworld_descriptions(task_name: str) -> dict[str, str]:
# MetaWorld tasks don't expose a separate NL description attribute;
# use a cleaned version of the task name as the description.
label = task_name.removeprefix("metaworld-").replace("-", " ").strip()
return {f"{task_name}_0": label}
def _robotwin_descriptions(task_names: str) -> dict[str, str]:
"""Return descriptions for each requested RoboTwin task. Reads
`description/task_instruction/<task>.json` from the RoboTwin clone
(cwd is /opt/robotwin in CI). Falls back to the task name if missing."""
out: dict[str, str] = {}
root = Path("description/task_instruction")
for name in (t.strip() for t in task_names.split(",") if t.strip()):
desc_file = root / f"{name}.json"
desc = name.replace("_", " ")
if desc_file.is_file():
data = json.loads(desc_file.read_text())
full = data.get("full_description") or desc
# Strip the schema placeholders ({A}, {a}) — keep the sentence readable.
desc = full.replace("<", "").replace(">", "")
out[f"{name}_0"] = desc
return out
def _robocasa_descriptions(task_spec: str) -> dict[str, str]:
"""For each task in the comma-separated list, emit a cleaned-name label.
RoboCasa episodes carry their language instruction in the env's
`ep_meta['lang']`, populated per reset. Pulling it requires spinning
up the full kitchen env per task (~seconds each); we use the task
name as the key here and let the eval's episode info carry the
actual instruction.
"""
out: dict[str, str] = {}
for task in (t.strip() for t in task_spec.split(",") if t.strip()):
# Split CamelCase into words: "CloseFridge" → "close fridge".
label = "".join(f" {c.lower()}" if c.isupper() else c for c in task).strip()
out[f"{task}_0"] = label or task
return out
_ROBOMME_DESCRIPTIONS = {
"BinFill": "Fill the target bin with the correct number of cubes",
"PickXtimes": "Pick the indicated cube the specified number of times",
"SwingXtimes": "Swing the object the specified number of times",
"StopCube": "Grasp and stop the moving cube",
"VideoUnmask": "Pick the cube shown in the reference video",
"VideoUnmaskSwap": "Pick the cube matching the reference video after a swap",
"ButtonUnmask": "Press the button indicated by the reference",
"ButtonUnmaskSwap": "Press the correct button after objects are swapped",
"PickHighlight": "Pick the highlighted cube",
"VideoRepick": "Repick the cube shown in the reference video",
"VideoPlaceButton": "Place the cube on the button shown in the video",
"VideoPlaceOrder": "Place cubes in the order shown in the video",
"MoveCube": "Move the cube to the target location",
"InsertPeg": "Insert the peg into the target hole",
"PatternLock": "Unlock the pattern by pressing buttons in sequence",
"RouteStick": "Route the stick through the required waypoints",
}
def _robomme_descriptions(task_names: str, task_ids: list[int] | None = None) -> dict[str, str]:
"""Return descriptions for each requested RoboMME task. Keys match the
video filename pattern `<task>_<task_id>` used by the eval script."""
if task_ids is None:
task_ids = [0]
out: dict[str, str] = {}
for name in (t.strip() for t in task_names.split(",") if t.strip()):
desc = _ROBOMME_DESCRIPTIONS.get(name, name)
for tid in task_ids:
out[f"{name}_{tid}"] = desc
return out
def main() -> int:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--env", required=True, help="Environment family (libero, metaworld, ...)")
parser.add_argument("--task", required=True, help="Task/suite name (e.g. libero_spatial)")
parser.add_argument(
"--task-ids",
type=str,
default=None,
help="Comma-separated task IDs (e.g. '0,1,2'). Default: [0]",
)
parser.add_argument("--output", required=True, help="Path to write task_descriptions.json")
args = parser.parse_args()
task_ids: list[int] | None = None
if args.task_ids:
task_ids = [int(x.strip()) for x in args.task_ids.split(",")]
descriptions: dict[str, str] = {}
try:
if args.env == "libero":
descriptions = _libero_descriptions(args.task)
elif args.env == "metaworld":
descriptions = _metaworld_descriptions(args.task)
elif args.env == "robotwin":
descriptions = _robotwin_descriptions(args.task)
elif args.env == "robocasa":
descriptions = _robocasa_descriptions(args.task)
elif args.env == "robomme":
descriptions = _robomme_descriptions(args.task, task_ids=task_ids)
else:
print(
f"[extract_task_descriptions] No description extractor for env '{args.env}'.",
file=sys.stderr,
)
except Exception as exc:
print(f"[extract_task_descriptions] Warning: {exc}", file=sys.stderr)
out_path = Path(args.output)
out_path.parent.mkdir(parents=True, exist_ok=True)
out_path.write_text(json.dumps(descriptions, indent=2))
print(f"[extract_task_descriptions] {len(descriptions)} descriptions → {out_path}")
return 0
if __name__ == "__main__":
sys.exit(main())