train: switch EMA from custom ModelEMA to ema-pytorch

Replace the 250-line src/lerobot/utils/ema.py with a direct dependency
on ema-pytorch (lucidrains' canonical PyTorch EMA library). Same
semantics, decay=0.999 default unchanged, but offloads the maintenance
burden to a maintained library used by every diffusion repo.

Why ema-pytorch:
  * Standard PyTorch EMA library; battle-tested across diffusion +
    speech + image-gen codebases.
  * Tiny pure-python dep (no compiled code).
  * Cleaner consumer-side API: ema.ema_model is a full nn.Module
    clone of the policy, so eval / wandb just pass it through instead
    of context-managed swap/restore on the live model.

What changed mechanically:
  * pyproject.toml: add 'ema-pytorch>=0.7.7,<1.0.0' to core deps.
  * deleted src/lerobot/utils/ema.py (the custom ModelEMA).
  * scripts/lerobot_train.py:
      - import EMA from ema_pytorch
      - instantiate with beta=cfg.ema.decay,
        update_after_step=cfg.ema.warmup_steps, update_every=1,
        include_online_model=False (accelerator owns live model
        lifecycle; double-registration would double-count params).
      - ema.update() (no args) — library tracks the online model
        internally.
      - Eval block: pass eval_target_policy = ema.ema_model (when
        cfg.ema.use_for_eval) instead of swap context manager.
      - W&B examples: same pattern.
      - Save: torch.save(ema.state_dict(), .../ema_state.pt) instead
        of custom safetensors writer. .pt format is consistent with
        the rest of training_state which already mixes safetensors +
        json + (now) pt.
      - Resume: ema.load_state_dict(torch.load(.../ema_state.pt)).
      - WandB observability: ema/step (count of ema.update calls),
        ema/initted (bool from library), ema/beta (constant from
        cfg).
  * configs/default.py: EMAConfig.decay stays 0.999 (matches
    openpi's pi05_libero); docstring updated to reflect ema-pytrch
    semantics for warmup_steps (now maps to update_after_step — a hard
    skip, not a smooth decay ramp).

Behavior preserved:
  * Defaults: enable=False, decay=0.999, warmup_steps=0,
    use_for_eval=True, use_for_wandb_examples=True.
  * Same CLI: --ema.enable=true, --ema.decay=X, etc.
  * Same checkpoint layout (training_state/ema_state.pt next to
    optimizer_state.safetensors etc.); resumes silently if present.

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

pyproject.toml

@@ -85,6 +85,11 @@ dependencies = [
     "termcolor>=2.4.0,<4.0.0",
     "tqdm>=4.66.0,<5.0.0",
 
+    # Training utilities
+    # EMA of policy parameters (Diffusion Policy / pi05 style). Tiny
+    # pure-python dependency — preferred over a hand-rolled implementation.
+    "ema-pytorch>=0.7.7,<1.0.0",
+
     # Build tools (required by opencv-python-headless on some platforms)
     "cmake>=3.29.0.1,<4.2.0",
     "setuptools>=71.0.0,<81.0.0",