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>

pyproject.toml

@@ -164,6 +164,7 @@ pynput-dep = ["pynput>=1.7.8,<1.9.0"]
 pyzmq-dep = ["pyzmq>=26.2.1,<28.0.0"]
 motorbridge-dep = ["motorbridge>=0.3.2,<0.4.0"]
 motorbridge-smart-servo-dep = ["motorbridge-smart-servo>=0.0.4,<0.1.0"]
+timm-dep = ["timm>=1.0.0,<1.1.0"]
 
 # Motors
 feetech = ["feetech-servo-sdk>=1.0.0,<2.0.0", "lerobot[pyserial-dep]", "lerobot[deepdiff-dep]"]
@@ -221,7 +222,7 @@ groot = [
     "lerobot[diffusers-dep]",
     "lerobot[dataset]", # NOTE: processor_groot builds a LeRobotDataset for relative-action training stats
     "dm-tree>=0.1.8,<1.0.0",
-    "timm>=1.0.0,<1.1.0",
+    "lerobot[timm-dep]",
     "decord>=0.6.0,<1.0.0; (platform_machine == 'AMD64' or platform_machine == 'x86_64')",
 ]
 sarm = ["lerobot[transformers-dep]", "pydantic>=2.0.0,<3.0.0", "faker>=33.0.0,<35.0.0", "lerobot[matplotlib-dep]", "lerobot[qwen-vl-utils-dep]"]
@@ -233,6 +234,7 @@ fastwam = [
     "lerobot[transformers-dep]",
     "lerobot[diffusers-dep]",
 ]
+evo1 = ["lerobot[transformers-dep]"]
 hilserl = ["lerobot[transformers-dep]", "lerobot[dataset]", "gym-hil>=0.1.14,<0.2.0", "lerobot[grpcio-dep]", "lerobot[placo-dep]"]
 vla_jepa = ["lerobot[transformers-dep]", "lerobot[diffusers-dep]", "lerobot[qwen-vl-utils-dep]"]
 lingbot_va = ["lerobot[transformers-dep]", "lerobot[diffusers-dep]", "lerobot[accelerate-dep]"]
@@ -316,6 +318,7 @@ all = [
     "lerobot[fastwam]",
     "lerobot[groot]",
     "lerobot[xvla]",
+    "lerobot[evo1]",
     "lerobot[hilserl]",
     "lerobot[vla_jepa]",
     "lerobot[lingbot_va]",