refactor: replace rotation vector with body up vector for tilt input

- Replace body_attitude() with body_up_vector(): rotate world-up [0,0,1]
  by corrected world→body quaternion to get body up vector (pitch/roll only,
  no yaw). Matches DiffPhysDrone's env.R[:, 2] approach.
- Update ComputeTilt transform to use body_up_vector_np
- Update visualize_dataset.py to display Euler angles and body up vector
- Update model.py comments and disable CNN (zero output)
- Sync AGENTS.md with new architecture description

Generated by Mistral Vibe (ds-v4-flash).
Co-Authored-By: Mistral Vibe <vibe@mistral.ai>

src/velocity_prediction/model.py

@@ -1,9 +1,9 @@
 """
-VelocityPredictionModel: CNN + PoseMLP → concat → GRU → Head → [vx_body, vy_body].
+VelocityPredictionModel: CNN + PoseMLP → concat → GRU → Head → [v_right, v_forward].
 
 Architecture:
     Event frame (1, H, W) ──► CNN ──┐
-    Tilt angles    (3,)    ──► MLP ──┤──► concat ──► GRU ──► Head ──► [vx, vy]
+    Tilt angles    (3,)    ──► MLP ──┤──► concat ──► GRU ──► Head ──► [v_right, v_forward]
 """
 
 import torch
@@ -92,7 +92,7 @@ class VelocityPredictionModel(nn.Module):
         events: (B, S, 1, H, W)
         tilt:   (B, S, 3)
     Output:
-        v_body: (B, 2)  — body-frame [vx, vy] for the last frame in the sequence
+        v_body: (B, 2)  — body-frame [v_forward, v_lateral] for the last frame in the sequence
     """
 
     def __init__(self, cnn_cfg=model_cfg.cnn, pose_cfg=model_cfg.pose_mlp,
@@ -129,10 +129,13 @@ class VelocityPredictionModel(nn.Module):
             tilt:   (B, S, 3)
 
         Returns:
-            v_body: (B, 2)  predicted body-frame [vx, vy] at the last timestep
+            v_body: (B, 2)  predicted body-frame [v_forward, v_lateral] at the last timestep
         """
         # Per-frame encoding
-        cnn_feat = self.cnn(events)           # (B, S, 256)
+        # cnn_feat = self.cnn(events)           # (B, S, 256)
+        B, S = events.shape[:2]
+        cnn_feat = events.new_zeros(B, S, self.cnn.out_dim) # 全零替代
+
         pose_feat = self.pose_mlp(tilt)       # (B, S, 64)
 
         # Fuse per frame