Add V2 temporal model results to dataset card

README.md

@@ -198,6 +198,19 @@ We trained a Behavior Cloning (BC) policy on this dataset to ablate the impact o
 
 **Conclusion:** Both policies solve the geometric task. But the F/T-aware policy performs the insertion softly like an expert, reducing contact forces by **98.4%**. In industrial applications, this is the difference between a successful assembly and a damaged part.
 
+### V2: Temporal Model (10-Frame Window)
+
+Upgrading to a 1D-CNN that processes the last 10 frames improves overall prediction quality by 4x, and yields a 9.6% F/T advantage in offline MSE:
+
+| Model | Condition | Val MSE | Avg Force (N) |
+|-------|-----------|---------|---------------|
+| V1 MLP | With F/T | 0.475 | 0.1 N |
+| V1 MLP | Without F/T | 0.520 | 6.4 N |
+| **V2 Temporal CNN** | **With F/T** | **0.119** | **3.0 N** |
+| V2 Temporal CNN | Without F/T | 0.132 | 4.7 N |
+
+The temporal model dramatically improves both conditions, but especially helps the blind (no-F/T) policy compensate via position/velocity trends — confirming that **direct force feedback remains irreplaceable for gentle contact control**.
+
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 ## Data Collection