feat(groot): activate checkpoint-configured N1.7 raw-state dropout during training

Isaac-GR00T applies dual state regularization during fine-tuning: raw-state
zeroing driven by the processor sidecar's state_dropout_prob (0.2 for the
inspected N1.7 checkpoint) plus encoded-feature dropout. Baseline LeRobot kept
the processor in deterministic mode, so the raw-state dropout never activated
(RCA Tier-2 contributor to the LeRobot-trained SO-101 failures).

- GrootN17PackInputsStep: runtime-only 'training' flag + state_dropout_prob;
  whole-sample state zeroing gated on torch.is_grad_enabled() so eval and
  no_grad validation paths are unaffected
- sidecar loader reads state_dropout_prob from processor_config.json
- state_dropout_prob serializes with the step; the training flag intentionally
  does not (reloaded pipelines default to eval, re-enabled only when processors
  are rebuilt with dataset_meta)
- _set_groot_preprocessor_training toggles any dataclass step exposing a
  'training' field on serialized-pipeline reloads

Verification: tests/policies/groot/test_groot_state_dropout.py (4 passed) on
RTX PRO 6000 / CUDA 13.3.

tests/policies/groot/test_groot_state_dropout.py

@@ -0,0 +1,100 @@
+#!/usr/bin/env python
+
+# Copyright 2026 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.
+
+"""Isaac-GR00T N1.7 raw-state dropout training contract.
+
+Isaac-GR00T zeroes the entire proprioceptive state of a sample with probability
+``state_dropout_prob`` (configured in the checkpoint's processor sidecar) during
+training only. Baseline LeRobot kept the processor deterministic, so this
+regularization never activated. These tests pin the train/eval split.
+"""
+
+import torch
+
+from lerobot.policies.groot.processor_groot import GrootN17PackInputsStep
+from lerobot.types import TransitionKey
+from lerobot.utils.constants import OBS_STATE
+
+
+def _make_transition():
+    return {
+        TransitionKey.OBSERVATION: {OBS_STATE: torch.tensor([[1.0, 2.0], [3.0, 4.0]])},
+        TransitionKey.COMPLEMENTARY_DATA: {"task": ["Move", "Move"]},
+    }
+
+
+def test_groot_n1_7_training_applies_raw_state_dropout_before_encoder():
+    step = GrootN17PackInputsStep(
+        max_state_dim=4,
+        max_action_dim=4,
+        normalize_min_max=False,
+        training=True,
+        state_dropout_prob=1.0,
+    )
+
+    output = step(_make_transition())
+
+    expected = torch.zeros(2, 1, 4)
+    torch.testing.assert_close(output[TransitionKey.OBSERVATION]["state"], expected)
+
+
+def test_groot_n1_7_training_state_dropout_is_disabled_under_no_grad():
+    step = GrootN17PackInputsStep(
+        max_state_dim=4,
+        max_action_dim=4,
+        normalize_min_max=False,
+        training=True,
+        state_dropout_prob=1.0,
+    )
+
+    with torch.no_grad():
+        output = step(_make_transition())
+
+    expected = torch.tensor([[[1.0, 2.0, 0.0, 0.0]], [[3.0, 4.0, 0.0, 0.0]]])
+    torch.testing.assert_close(output[TransitionKey.OBSERVATION]["state"], expected)
+
+
+def test_groot_n1_7_eval_mode_state_dropout_is_inactive():
+    step = GrootN17PackInputsStep(
+        max_state_dim=4,
+        max_action_dim=4,
+        normalize_min_max=False,
+        training=False,
+        state_dropout_prob=1.0,
+    )
+
+    output = step(_make_transition())
+
+    expected = torch.tensor([[[1.0, 2.0, 0.0, 0.0]], [[3.0, 4.0, 0.0, 0.0]]])
+    torch.testing.assert_close(output[TransitionKey.OBSERVATION]["state"], expected)
+
+
+def test_groot_n1_7_pack_step_serializes_dropout_prob_but_not_training_mode():
+    step = GrootN17PackInputsStep(
+        max_state_dim=4,
+        max_action_dim=4,
+        normalize_min_max=False,
+        training=True,
+        state_dropout_prob=0.2,
+    )
+
+    serialized = step.get_config()
+    restored = GrootN17PackInputsStep(**serialized)
+
+    assert "training" not in serialized
+    assert serialized["state_dropout_prob"] == 0.2
+    assert restored.training is False
+    assert restored.state_dropout_prob == 0.2