feat(actpred): 固定范围归一化 + 逐列加权损失，附 pybot*actpred 审计修复

新增 action_norm.py 固定物理范围 min-max 归一化，无 stats 文件零状态部署；train.py 引入 _weighted_mse 逐列加权（舵机 1.0/spk 0.2/保留位 0.0）。审计修复: P0-1 pybot 读 normalize_mode, R2-1 NaN 阻断 best, P2-a 后端大小写不敏感, P1-3 utf-8-sig BOM, R1-1 data_dir 覆盖, R2-7 _run.sh 报错, R2-3 空 val 存 best.pt, P2-a2 误报警, R1-2 合并 parse_action_history, P2-b 合并 numpy import。评估修正 oob 物理界+error_growth 按 episode 中位数。推理部署全链支持 fixed_range。

pybot/pybot/main.py

@@ -141,7 +141,8 @@ def _load_predictor(checkpoint):
     """
     import os
     try:
-        if checkpoint.endswith(".hef"):
+        lower = checkpoint.lower()
+        if lower.endswith(".hef"):
             from actpred.hailo_infer import HailoPredictor as _P
             hef_dir = os.path.dirname(os.path.abspath(checkpoint))
 
@@ -160,6 +161,7 @@ def _load_predictor(checkpoint):
 
             # predict_delta：从同目录 checkpoint 读取训练配置
             predict_delta = False
+            normalize_mode = "zscore"
             for name in ("pretrain_best_inference.pt",
                          "best_inference.pt"):
                 ckpt_path = os.path.join(hef_dir, name)
@@ -171,9 +173,15 @@ def _load_predictor(checkpoint):
                         cfg = ck.get("config", {})
                         predict_delta = cfg.get("train", {}).get(
                             "predict_delta", False)
+                        normalize_mode = cfg.get("data", {}).get(
+                            "normalize_mode", "zscore")
                         if predict_delta:
                             logging.info(
                                 "predict_delta=True from %s", ckpt_path)
+                        if normalize_mode != "zscore":
+                            logging.info(
+                                "normalize_mode=%s from %s",
+                                normalize_mode, ckpt_path)
                     except Exception:
                         pass
                     break
@@ -181,7 +189,8 @@ def _load_predictor(checkpoint):
             try:
                 return _P(checkpoint, stats_path=stats_path,
                           predict_delta=predict_delta,
-                          pred_token=pred_token)
+                          pred_token=pred_token,
+                          normalize_mode=normalize_mode)
             except Exception:
                 logging.exception("加载 HEF 推理器失败: %s", checkpoint)
                 return None

pybot/pybot/robot.py

@@ -14,6 +14,8 @@ import logging
 import time
 from collections import deque
 
+import numpy as np
+
 from . import plugin
 from .hardvars import (AliasVar, CameraVar, I2CVar, MPU6050Var, ServoVar,
                        SpeakerVar)
@@ -238,7 +240,6 @@ class RobotStruct:
         （会阻塞 ~1/framerate 秒，把 hz 锁死在相机帧率之下）。dry_run
         或队列空时回退 _grab_frame 合成帧。
         """
-        import numpy as np
         camera_var = self._find_camera()
         if camera_var is None and not self.dry_run:
             raise ValueError("闭环需要 @Camera 块（或使用 --dry-run 生成合成帧）")
@@ -357,7 +358,6 @@ class RobotStruct:
         帧源与主循环一致（_loop_recent_frames），避免冷启动与运行期帧源
         不一致。history 用零向量填满是保守初值，避免随机姿态跳变损坏舵机。
         """
-        import numpy as np
         frames.extend(self._loop_recent_frames(camera_var, frames.maxlen))
         for _ in range(history.maxlen):
             history.append(np.zeros(action_dim, dtype=np.float32))
@@ -368,7 +368,6 @@ class RobotStruct:
         SpeakerVar.play 强制要求 delay 键；闭环已剔除 delay 列（避免发声
         阻塞卡死节拍），此处为其补 delay=0 —— 发声但不阻塞，节拍由 hz 掌控。
         """
-        import numpy as np
         if not np.all(np.isfinite(act)):
             log.warning("action vector contains NaN/Inf, skipping step")
             return
@@ -442,7 +441,6 @@ class RobotStruct:
         """
         if not imu_vars:
             return None
-        import numpy as np
         nk = len(IMU_KEYS)
         out = np.zeros(nk * len(imu_vars), dtype=np.float32)
         for k, var in enumerate(imu_vars):
@@ -458,7 +456,6 @@ class RobotStruct:
     @staticmethod
     def _pad_imu(vec, imu_dim):
         """拼接姿态向量 → pad/truncate 到 predictor 的 imu_dim（None → 全零）"""
-        import numpy as np
         out = np.zeros(imu_dim, dtype=np.float32)
         if vec is not None:
             src = np.asarray(vec, dtype=np.float32).ravel()
@@ -468,7 +465,6 @@ class RobotStruct:
 
     def _build_action_vec(self, vals, action_dim):
         """从值列表构建动作向量，pad/truncate 到 action_dim"""
-        import numpy as np
         vec = np.zeros(action_dim, dtype=np.float32)
         n = min(len(vals), action_dim)
         vec[:n] = vals[:n]
@@ -476,7 +472,6 @@ class RobotStruct:
 
     def _grab_frame(self, camera_var):
         """从摄像头同步抓一帧；dry_run 或无帧时返回随机噪声帧"""
-        import numpy as np
         if camera_var is not None:
             frame = camera_var.camera.capture()
             if frame is not None: