feat(pybot×actpred): R2 姿态反馈方案一——MPU6050 独立 IMU token 全链路

- pybot：recorder 逐帧落盘 imu.npy（T×8，roll,pitch,ax,ay,az,gx,gy,gz，与帧/动作同调用对齐）；run_loop 对声明 imu_dim 的 predictor 以 predict(..., imu=) 调用（鸭子类型，旧两参调用向后兼容）

- actpred：新增 --imu-dim；model 序列 [img×F, act×H, (IMU), PRED]（imu_dim>0 时 F+H+2，IMU token 位于 PRED 前）；dataset/train 读 imu.npy（缺失按零向量兜底）；predict/ONNX/Hailo 三后端与 export/gen_calibration/dfc_compile/_npu_test 部署链适配 imu 输入

- 姿态按物理量程固定缩放归一化（imu_norm.py，训练/推理共用，零状态）

- 测试：actpred 128 passed / pybot loop+recorder 28 passed

actpred/export_onnx.py

@@ -23,31 +23,38 @@ def main():
     dummy_images = torch.randn(
         1, data_cfg.frame_len, 3, data_cfg.image_height, data_cfg.image_width)
     dummy_history = torch.randn(1, data_cfg.history_len, data_cfg.action_dim)
+    # R2 方案一：imu_dim>0 时追加归一化姿态输入 (1, imu_dim)
+    imu_dim = data_cfg.imu_dim
+    extra_inputs = (torch.randn(1, imu_dim),) if imu_dim > 0 else ()
+    extra_names = ["imu"] if imu_dim > 0 else []
 
     batch = Dim("batch")
+    dynamic = {"images": {0: batch}, "action_history": {0: batch}}
+    if imu_dim > 0:
+        dynamic["imu"] = {0: batch}
     torch.onnx.export(
-        model, (dummy_images, dummy_history),
+        model, (dummy_images, dummy_history, *extra_inputs),
         args.output,
-        input_names=["images", "action_history"],
+        input_names=["images", "action_history"] + extra_names,
         output_names=["predicted_action"],
-        dynamic_shapes={
-            "images": {0: batch},
-            "action_history": {0: batch},
-        },
+        dynamic_shapes=dynamic,
         opset_version=18,
     )
-    print(f"ONNX model exported to: {args.output}")
+    print(f"ONNX model exported to: {args.output}"
+          f"{'（含 imu 输入）' if imu_dim > 0 else ''}")
 
     # 可选：验证导出的 ONNX 模型一致性
     try:
         import onnxruntime as ort
         import numpy as np
         sess = ort.InferenceSession(args.output)
-        img_np = dummy_images.numpy()
-        hist_np = dummy_history.numpy()
-        ort_out = sess.run(None, {"images": img_np, "action_history": hist_np})[0]
+        feeds = {"images": dummy_images.numpy(),
+                 "action_history": dummy_history.numpy()}
+        for name, t in zip(extra_names, extra_inputs):
+            feeds[name] = t.numpy()
+        ort_out = sess.run(None, feeds)[0]
         with torch.no_grad():
-            torch_out = model(dummy_images, dummy_history).numpy()
+            torch_out = model(dummy_images, dummy_history, *extra_inputs).numpy()
         max_diff = np.abs(ort_out - torch_out).max()
         print(f"ONNX 一致性验证通过（最大差异: {max_diff:.6e}）")
     except ImportError: