Merge branch 'feat/language-annotation-pipeline' into feat/smolvla-on-steerable

Resolves conflicts from 66 commits on the base branch:

* pyproject.toml — keep base's transformers>=5.4.0,<5.6.0; add the
  sentencepiece-dep entry pi052 (FAST action tokenizer) needs.
* policies/__init__.py — keep pi052 export; drop the
  RewardClassifierConfig export that base removed.
* policies/factory.py — docstring list resolution (keep pi052; drop
  reward_classifier, removed by base).
* annotations/steerable_pipeline/executor.py — adopt base's renamed
  _ensure_annotation_metadata_in_info (it already advertises the say
  tool); drop pi052's older _ensure_tools_in_info call.
* configs/train.py — keep pi052's vqa_target_fraction; adopt base's
  SampleWeightingConfig (legacy RA-BC inline params already covered
  by the migration shim base added).
* scripts/lerobot_train.py — merge pi052's per-policy processor
  rebuild + dataset_repo_id pass-through with base's active_cfg /
  is_reward_model_training tightening, and re-route vqa-weighted
  sampler to active_cfg.drop_n_last_frames.
* datasets/language_render.py — adopt base's _select_one + timestamp
  tolerance (drops pi052's stale _select_latest / per-style sort_key).
* tests — adopt base's parametrized per-camera blend + tolerance
  test; drop pi052 tests that overlap with base's tighter rewrites;
  keep pi052's flow-only / VQA-blend coverage; add a
  test_canonical_recipe_loads check on subtask_mem_vqa_speech.yaml.
* policies/pi052/processor_pi052.py — import RenderMessagesStep
  directly from render_messages_processor (base intentionally
  dropped it from lerobot.processor's re-exports).
* uv.lock — regenerated cleanly from base + pi052's pocket-tts /
  beartype.

All 67 touched tests pass (30 pi052 + 37 recipe / language-render /
pipeline / render-messages).

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

tests/annotations/test_pipeline_recipe_render.py

@@ -23,9 +23,9 @@ import pyarrow.parquet as pq
 
 from lerobot.annotations.steerable_pipeline.config import (
     AnnotationPipelineConfig,
-    Module1Config,
-    Module2Config,
-    Module3Config,
+    InterjectionsConfig,
+    PlanConfig,
+    VqaConfig,
 )
 from lerobot.annotations.steerable_pipeline.executor import Executor
 from lerobot.annotations.steerable_pipeline.modules import (
@@ -35,19 +35,59 @@ from lerobot.annotations.steerable_pipeline.modules import (
 )
 from lerobot.annotations.steerable_pipeline.validator import StagingValidator
 from lerobot.annotations.steerable_pipeline.writer import LanguageColumnsWriter
-from lerobot.configs.recipe import TrainingRecipe
+from lerobot.configs.recipe import MessageTurn, TrainingRecipe
 from lerobot.datasets.language_render import render_sample
 
 from ._helpers import make_canned_responder
 
-_RECIPE_PATH = (
-    Path(__file__).resolve().parents[2]
-    / "src"
-    / "lerobot"
-    / "configs"
-    / "recipes"
-    / "subtask_mem_vqa_speech.yaml"
-)
+def _build_pr1_style_blend_recipe() -> TrainingRecipe:
+    """Inline blend recipe that consumes every style this pipeline produces.
+
+    PR 1 used to ship ``src/lerobot/configs/recipes/pi05_hirobot.yaml`` as
+    a canonical example, but that file was dropped during PR 1 review. The
+    cross-PR contract this test guards is "the recipe DSL can render
+    non-empty messages from pipeline output", which doesn't require a
+    specific YAML — so we build the equivalent blend in code.
+    """
+    return TrainingRecipe(
+        blend={
+            "low_level_execution": TrainingRecipe(
+                weight=0.35,
+                messages=[
+                    MessageTurn(
+                        role="user",
+                        content="${task}\nPlan: ${plan}\nMemory: ${memory}",
+                        stream="high_level",
+                    ),
+                    MessageTurn(role="assistant", content="${subtask}", stream="low_level", target=True),
+                ],
+            ),
+            "user_interjection_response": TrainingRecipe(
+                weight=0.16,
+                bindings={
+                    "speech": "emitted_at(t, role=assistant, tool_name=say)",
+                    "interjection": "emitted_at(t, style=interjection)",
+                },
+                messages=[
+                    MessageTurn(role="user", content="${task}", stream="high_level"),
+                    MessageTurn(
+                        role="user",
+                        content="${interjection}",
+                        stream="high_level",
+                        if_present="interjection",
+                    ),
+                    MessageTurn(
+                        role="assistant",
+                        content="${plan}",
+                        stream="high_level",
+                        target=True,
+                        if_present="plan",
+                        tool_calls_from="speech",
+                    ),
+                ],
+            ),
+        }
+    )
 
 
 def _build_executor() -> Executor:
@@ -74,15 +114,15 @@ def _build_executor() -> Executor:
         },
     )
     config = AnnotationPipelineConfig(
-        module_1=Module1Config(),
-        module_2=Module2Config(max_interjections_per_episode=1, interjection_min_t=0.5),
-        module_3=Module3Config(vqa_emission_hz=1.0, K=2),
+        plan=PlanConfig(),
+        interjections=InterjectionsConfig(max_interjections_per_episode=1, interjection_min_t=0.5),
+        vqa=VqaConfig(vqa_emission_hz=1.0, K=2),
     )
     return Executor(
         config=config,
-        module_1=PlanSubtasksMemoryModule(vlm=vlm, config=config.module_1),
-        module_2=InterjectionsAndSpeechModule(vlm=vlm, config=config.module_2, seed=config.seed),
-        module_3=GeneralVqaModule(vlm=vlm, config=config.module_3, seed=config.seed),
+        plan=PlanSubtasksMemoryModule(vlm=vlm, config=config.plan),
+        interjections=InterjectionsAndSpeechModule(vlm=vlm, config=config.interjections, seed=config.seed),
+        vqa=GeneralVqaModule(vlm=vlm, config=config.vqa, seed=config.seed),
         writer=LanguageColumnsWriter(),
         validator=StagingValidator(),
     )
@@ -101,13 +141,7 @@ def test_pr1_canonical_recipe_renders_nonempty_from_pipeline_output(
     events_lists = table.column("language_events").to_pylist()
     timestamps = table.column("timestamp").to_pylist()
 
-    recipe = TrainingRecipe.from_yaml(_RECIPE_PATH) if hasattr(TrainingRecipe, "from_yaml") else None
-    if recipe is None:
-        # PR 1 may not expose from_yaml; load via PyYAML and TrainingRecipe(**...)
-        import yaml
-
-        loaded = yaml.safe_load(_RECIPE_PATH.read_text(encoding="utf-8"))
-        recipe = TrainingRecipe(**loaded)
+    recipe = _build_pr1_style_blend_recipe()
 
     rendered_any = False
     for sample_idx, (ts, persistent, events) in enumerate(