feat(policies): add EVO1 policy (#3908)

* feat(policies): add EVO1 policy

* fix(evo1): infer batch size after normalizing image dims

`_collect_image_batches` read `batch_size = batch[camera_keys[0]].shape[0]`
before normalizing per-camera tensors to `(B, C, H, W)`. For an unbatched
`(C, H, W)` input (which the function tries to support via the `image.dim() == 3`
branch), this picked up the channel count `C` instead of the real batch size,
making the subsequent per-sample loop iterate `C` times and indexing go
out of bounds.

Normalize each camera tensor up-front, then read `batch_size` from the
normalized batch dim. Adds `test_collect_image_batches_handles_unbatched_chw`
covering the regression.

Reported by Copilot review on huggingface/lerobot#3545.

* chore(lock): regenerate uv.lock for evo1 extra

Adds the `evo1` entry to `[package.metadata.requires-dist]` and the
`provides-extras` list so that `uv sync --locked --extra test` (used by
fast_tests.yml) no longer reports the lockfile as stale.

Generated with `uv 0.8.0` (matching `UV_VERSION` in fast_tests.yml).
The non-evo1 marker tweaks are produced by `uv lock` re-resolving the
existing dep graph and are not introduced by this PR.

* chore(evo1): align with policy contribution guide conventions

- Add `src/lerobot/policies/evo1/README.md` symlink into `docs/source/evo1.mdx`
  to match the in-tree README convention (mirroring the EO-1 layout).
- Convert `transformers` import in `internvl3_embedder.py` to the standard
  `TYPE_CHECKING + _transformers_available` two-step gating used by other
  optional-backbone policies (e.g. diffusion). The previous lazy-in-`__init__`
  import was functionally equivalent for runtime gating but didn't expose the
  real symbols to type checkers.
- Add `lerobot[evo1]` to the `all` extra in `pyproject.toml` so
  `pip install 'lerobot[all]'` keeps installing every optional policy.

Per the guidance in https://moon-ci-docs.huggingface.co/docs/lerobot/pr_3534/en/contributing_a_policy.

* fix(evo1): finalize policy guide alignment

* docs(evo1): format results table

* Fix EVO1 LIBERO rollout processors

* Fix EVO1 LIBERO eval action postprocessing

* Fix eval action conversion for bf16 policies

* fix(evo1): move LIBERO padding into policy processors

* refactor(evo1): use native HF InternVL3-1B-hf, drop trust_remote_code

- Switch from OpenGVLab/InternVL3-1B (requires trust_remote_code=True)
  to OpenGVLab/InternVL3-1B-hf (native transformers implementation).
- Replace manual _extract_feature + _prepare_and_fuse_embeddings with
  a single model.forward() call — verified bit-for-bit identical output.
- Remove ~170 lines of manual ViT/pixel-shuffle/projection logic.
- Symlink README.md to docs/source/ following repo convention.

Weights are byte-identical between both model variants; only the module
naming differs. All 12 existing unit tests pass. Local training (10 steps)
on maximellerbach/omx_pickandplace confirmed working.

* refactor(policy): evo1 GPU-batched preprocessing +  vectorized attention masking + remove dead code

* fix(style): pre-commit

oops

* chore(evo1): delete added test + reduce diff

* refactor(policies): use config for evo1 + local imports

* refactor(policies): multiple improvements

* chore: update docs + remove legacy codepaths

* feat(policies): implement RTC to EVO1

---------

Co-authored-by: javadcc_mac <javadcc1@sjtu.edu.cn>
Co-authored-by: Yiming Wang <145452074+JAVAdcc@users.noreply.github.com>
Co-authored-by: Martino Russi <nopyeps@gmail.com>

tests/envs/test_dispatch.py

@@ -7,11 +7,14 @@ from dataclasses import dataclass, field
 
 import gymnasium as gym
 import pytest
+import torch
 from gymnasium.envs.registration import register, registry as gym_registry
 
 from lerobot.configs.types import PolicyFeature
-from lerobot.envs.configs import EnvConfig
+from lerobot.envs.configs import EnvConfig, LiberoEnv
 from lerobot.envs.factory import make_env, make_env_config, make_env_pre_post_processors
+from lerobot.processor import LiberoProcessorStep
+from lerobot.utils.constants import OBS_PREFIX, OBS_STATE
 
 logger = logging.getLogger(__name__)
 
@@ -61,6 +64,31 @@ def test_processors_delegation():
     assert len(pre.steps) == 0
 
 
+def test_libero_processors_are_policy_agnostic():
+    cfg = LiberoEnv()
+    pre, post = make_env_pre_post_processors(cfg, policy_cfg=object())
+
+    assert isinstance(pre.steps[0], LiberoProcessorStep)
+    assert len(post.steps) == 0
+
+
+def test_libero_processor_flattens_state_to_raw_8_dim():
+    step = LiberoProcessorStep()
+    observation = {
+        OBS_PREFIX + "robot_state": {
+            "eef": {
+                "pos": torch.tensor([[1.0, 2.0, 3.0]]),
+                "quat": torch.tensor([[0.0, 0.0, 0.0, 1.0]]),
+            },
+            "gripper": {"qpos": torch.tensor([[4.0, 5.0]])},
+        }
+    }
+
+    state = step.observation(observation)[OBS_STATE]
+    assert state.shape == (1, 8)
+    assert torch.allclose(state, torch.tensor([[1.0, 2.0, 3.0, 0.0, 0.0, 0.0, 4.0, 5.0]]))
+
+
 def test_base_create_envs():
     """Base class create_envs() should build a single-task VectorEnv via gym.make()."""
     gym_id = "_dispatch_test/CartPole-v99"