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https://github.com/huggingface/lerobot.git
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Pepijn
cec8ee0be6
Steerable annotation pipeline (lerobot-annotate) that populates the language_persistent and language_events columns introduced in PR 1 (#3467) directly into data/chunk-*/file-*.parquet. This is PR 2 of the three-PR plan: PR 1 (Add extensive language support #3467): schema + DSL + rendering, base of this PR PR 2 (this PR): annotation pipeline writing into PR 1's columns PR 3: model with language prediction and runtime A VLM (Qwen-VL family, served on vLLM) watches each episode's video and emits grounded language annotations: subtasks, plans, memory, task rephrasings, interjections + speech, and per-camera VQA. The pipeline is built for production annotation at scale — single-camera grounding, embedded-frame inputs, a describe-then-segment grounding flow, and a deterministic full-episode coverage guarantee — informed by Scale's dense-captioning findings (representation > sampling, rules > reasoning, model capacity is the biggest lever, two-pass systems compound errors)
59 lines
2.0 KiB
Python
59 lines
2.0 KiB
Python
#!/usr/bin/env python
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# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Helpers shared across annotation-pipeline tests."""
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from __future__ import annotations
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import json
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from typing import Any
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from lerobot.annotations.steerable_pipeline.vlm_client import StubVlmClient
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def make_canned_responder(
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responses_by_marker: dict[str, Any],
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default: Any = None,
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) -> StubVlmClient:
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"""Return a stub that picks a response by inspecting the user prompt.
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For each call the responder examines the last user-message text and
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returns the response keyed by the first marker substring it contains.
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Falls back to ``default`` if no marker matches.
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"""
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def responder(messages: list[dict[str, Any]]) -> Any:
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last_user_text = ""
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for message in messages:
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if message.get("role") != "user":
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continue
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content = message.get("content")
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if isinstance(content, str):
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last_user_text = content
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elif isinstance(content, list):
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for block in content:
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if isinstance(block, dict) and block.get("type") == "text":
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last_user_text = block.get("text", "")
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for marker, response in responses_by_marker.items():
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if marker in last_user_text:
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return response
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return default
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return StubVlmClient(responder=responder)
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def encode_vqa_answer(payload: dict[str, Any]) -> str:
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return json.dumps(payload, sort_keys=True)
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