feat(pi05): integrate RenderMessagesStep for advantage conditioning

Add RenderedMessagesToTaskStep adapter that bridges recipe-rendered chat
messages back into PI05's task-string prompt format. When recipe_path is
set on PI05Config, the preprocessor inserts RenderMessagesStep + adapter
before prompt construction, enabling RECAP advantage text to flow
end-to-end through the recipe YAML system.

tests/processor/test_rendered_messages_to_task.py

@@ -0,0 +1,186 @@
+#!/usr/bin/env python
+
+"""Tests for RenderedMessagesToTaskStep and PI05 pipeline integration with advantage."""
+
+import pytest
+
+pytest.importorskip("datasets", reason="datasets is required (install lerobot[dataset])")
+
+import torch  # noqa: E402
+
+from lerobot.configs.recipe import MessageTurn, TrainingRecipe  # noqa: E402
+from lerobot.processor.converters import create_transition  # noqa: E402
+from lerobot.processor.render_messages_processor import RenderMessagesStep  # noqa: E402
+from lerobot.processor.rendered_messages_to_task import RenderedMessagesToTaskStep  # noqa: E402
+from lerobot.types import TransitionKey  # noqa: E402
+
+
+def test_rendered_messages_to_task_noops_without_messages():
+    """Without messages key, the step is a no-op."""
+    transition = create_transition(complementary_data={"task": "pick up the cup"})
+    step = RenderedMessagesToTaskStep()
+    out = step(transition)
+    data = out[TransitionKey.COMPLEMENTARY_DATA]
+    assert data["task"] == "pick up the cup"
+
+
+def test_rendered_messages_to_task_extracts_user_content():
+    """Extracts user-role message content and joins with newline."""
+    transition = create_transition(
+        complementary_data={
+            "task": "original task",
+            "messages": [
+                {"role": "user", "content": "pick up the cup"},
+                {"role": "user", "content": "Advantage: positive"},
+                {"role": "assistant", "content": "reach for cup"},
+            ],
+            "message_streams": ["high_level", "high_level", "low_level"],
+            "target_message_indices": [2],
+        }
+    )
+    step = RenderedMessagesToTaskStep()
+    out = step(transition)
+    data = out[TransitionKey.COMPLEMENTARY_DATA]
+
+    assert data["task"] == "pick up the cup\nAdvantage: positive"
+    assert "messages" not in data
+    assert "message_streams" not in data
+    assert "target_message_indices" not in data
+
+
+def test_rendered_messages_to_task_handles_multimodal_blocks():
+    """Extracts text from HF multimodal content blocks."""
+    transition = create_transition(
+        complementary_data={
+            "task": "original",
+            "messages": [
+                {
+                    "role": "user",
+                    "content": [
+                        {"type": "image", "image": "placeholder"},
+                        {"type": "text", "text": "describe this"},
+                    ],
+                },
+                {"role": "assistant", "content": "a cup on a table"},
+            ],
+            "message_streams": ["high_level", "low_level"],
+            "target_message_indices": [1],
+        }
+    )
+    step = RenderedMessagesToTaskStep()
+    out = step(transition)
+    data = out[TransitionKey.COMPLEMENTARY_DATA]
+
+    assert data["task"] == "describe this"
+
+
+def test_rendered_messages_to_task_preserves_list_task_format():
+    """When original task is a list (batched), output is also a list."""
+    transition = create_transition(
+        complementary_data={
+            "task": ["task1", "task2"],
+            "messages": [
+                {"role": "user", "content": "rendered task"},
+                {"role": "assistant", "content": "do it", "target": True},
+            ],
+            "message_streams": ["high_level", "low_level"],
+            "target_message_indices": [1],
+        }
+    )
+    step = RenderedMessagesToTaskStep()
+    out = step(transition)
+    data = out[TransitionKey.COMPLEMENTARY_DATA]
+
+    assert data["task"] == ["rendered task", "rendered task"]
+
+
+def test_full_render_then_flatten_pipeline():
+    """RenderMessagesStep + RenderedMessagesToTaskStep produces correct task string."""
+    recipe = TrainingRecipe(
+        messages=[
+            MessageTurn(role="user", content="${task}", stream="high_level"),
+            MessageTurn(
+                role="user",
+                content="Advantage: ${advantage}",
+                stream="high_level",
+                if_present="advantage",
+            ),
+            MessageTurn(role="assistant", content="${subtask}", stream="low_level", target=True),
+        ]
+    )
+    transition = create_transition(
+        complementary_data={
+            "task": "pick up the cup",
+            "timestamp": torch.tensor(0.5),
+            "index": torch.tensor(0),
+            "language_persistent": [
+                {
+                    "role": "assistant",
+                    "content": "reach for the cup",
+                    "style": "subtask",
+                    "timestamp": 0.0,
+                    "camera": None,
+                    "tool_calls": None,
+                },
+                {
+                    "role": "user",
+                    "content": "positive",
+                    "style": "advantage",
+                    "timestamp": 0.1,
+                    "camera": None,
+                    "tool_calls": None,
+                },
+            ],
+            "language_events": [],
+        }
+    )
+
+    # Step 1: Render recipe
+    rendered = RenderMessagesStep(recipe=recipe)(transition)
+    # Step 2: Flatten to task string
+    out = RenderedMessagesToTaskStep()(rendered)
+    data = out[TransitionKey.COMPLEMENTARY_DATA]
+
+    assert "pick up the cup" in data["task"]
+    assert "Advantage: positive" in data["task"]
+
+
+def test_full_render_advantage_absent_skips_turn():
+    """When advantage row is absent, the advantage turn is skipped via if_present."""
+    recipe = TrainingRecipe(
+        messages=[
+            MessageTurn(role="user", content="${task}", stream="high_level"),
+            MessageTurn(
+                role="user",
+                content="Advantage: ${advantage}",
+                stream="high_level",
+                if_present="advantage",
+            ),
+            MessageTurn(role="assistant", content="${subtask}", stream="low_level", target=True),
+        ]
+    )
+    transition = create_transition(
+        complementary_data={
+            "task": "pick up the cup",
+            "timestamp": torch.tensor(0.5),
+            "index": torch.tensor(0),
+            "language_persistent": [
+                {
+                    "role": "assistant",
+                    "content": "reach for the cup",
+                    "style": "subtask",
+                    "timestamp": 0.0,
+                    "camera": None,
+                    "tool_calls": None,
+                },
+            ],
+            "language_events": [],
+        }
+    )
+
+    rendered = RenderMessagesStep(recipe=recipe)(transition)
+    out = RenderedMessagesToTaskStep()(rendered)
+    data = out[TransitionKey.COMPLEMENTARY_DATA]
+
+    assert data["task"] == "pick up the cup"
+    assert "Advantage" not in data["task"]