feat(eval): add per-episode timing logs to eval worker

Logs avg env step time, avg inference call time, and totals per episode
to identify whether env or policy server is the bottleneck.

Made-with: Cursor

src/lerobot/scripts/lerobot_eval_worker.py

@@ -35,6 +35,7 @@ from __future__ import annotations
 import json
 import logging
 import pickle  # nosec B403 — internal serialisation only
+import time
 import urllib.request
 from dataclasses import dataclass, field
 from pathlib import Path
@@ -130,15 +131,24 @@ def run_worker(cfg: EvalWorkerConfig) -> dict:
             ep_rewards: list[float] = []
             ep_success = False
             done = np.zeros(1, dtype=bool)
+            ep_steps = 0
+            ep_infer_time = 0.0
+            ep_env_time = 0.0
+            ep_infer_calls = 0
 
             while not np.all(done):
                 if not action_buffer:
+                    t0 = time.monotonic()
                     chunk_np = _call_server(cfg.server_address, obs_t, cfg.server_timeout)
-                    # chunk_np: (T, action_dim) — split into per-step slices of shape (1, action_dim)
+                    ep_infer_time += time.monotonic() - t0
+                    ep_infer_calls += 1
                     action_buffer = [chunk_np[i : i + 1] for i in range(chunk_np.shape[0])]
 
-                action_np = action_buffer.pop(0)  # (1, action_dim)
+                action_np = action_buffer.pop(0)
+                t0 = time.monotonic()
                 obs, reward, terminated, truncated, info = env.step(action_np)
+                ep_env_time += time.monotonic() - t0
+                ep_steps += 1
 
                 done = terminated | truncated | done
                 ep_rewards.append(float(np.mean(reward)))
@@ -155,13 +165,22 @@ def run_worker(cfg: EvalWorkerConfig) -> dict:
             sum_rewards.append(float(np.sum(ep_rewards)))
             max_rewards.append(float(np.max(ep_rewards)) if ep_rewards else 0.0)
             successes.append(ep_success)
+            avg_env_ms = (ep_env_time / ep_steps * 1000) if ep_steps else 0
+            avg_infer_ms = (ep_infer_time / ep_infer_calls * 1000) if ep_infer_calls else 0
             logger.info(
-                "Task %s[%d] ep %d/%d — success=%s",
+                "Task %s[%d] ep %d/%d — success=%s | %d steps, %d infer calls | "
+                "env %.0fms/step, infer %.0fms/call (env %.1fs, infer %.1fs total)",
                 task_group,
                 task_id,
                 ep_idx + 1,
                 cfg.n_episodes,
                 ep_success,
+                ep_steps,
+                ep_infer_calls,
+                avg_env_ms,
+                avg_infer_ms,
+                ep_env_time,
+                ep_infer_time,
             )
 
         per_task.append(