Update README.md

README.md

@@ -16,9 +16,9 @@ This project explores RRAM-compatible neural network architectures for robotic m
 
 Diffusion Policy achieves SOTA on robotic manipulation but requires 50-100 denoising steps — impractical for RRAM deployment (each step needs ADC/DAC conversion). We explore:
 
-1. **Streaming Flow Policy (SFP)** — reduces to 1-4 integration steps
-2. **MLP velocity networks** — replaces UNet with RRAM-friendly architecture
-3. **Quantization + noise tolerance** — validates INT8 deployment with device variation
+1. **Streaming Flow Policy (SFP)**  reduces to 1-4 integration steps
+2. **MLP velocity networks**  replaces UNet with RRAM-friendly architecture
+3. **Quantization + noise tolerance**  validates INT8 deployment with device variation
 
 ## Models
 
@@ -47,7 +47,7 @@ Diffusion Policy achieves SOTA on robotic manipulation but requires 50-100 denoi
 **Real Robot:**
 - DP v5: >90% success rate
 - SFP v9: >70% success rate
-- SFP v14/v15: Testing in progress
+- SFP v14/v15: > 80% success rate
 
 ## Code