---
license: apache-2.0
tags:
  - robotics
  - diffusion-policy
  - flow-matching
  - lerobot
  - rram
---

# FMLP-Policy: Flow Matching MLP for Robotic Control

This project explores RRAM-compatible neural network architectures for robotic manipulation policies, replacing UNet with pure MLP (Linear + ReLU only) for deployment on analog RRAM accelerators.

## Overview

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

## Models

| Model | Architecture | Description |
|-------|--------------|-------------|
| [pusht_diffusion_v3](https://huggingface.co/Liyux/pusht_diffusion_v3) | ResNet18 + UNet | DP baseline, 136 episodes |
| [pusht_diffusion_v4](https://huggingface.co/Liyux/pusht_diffusion_v4) | ResNet18 + UNet | DP baseline, 226 episodes |
| [pusht_diffusion_v5](https://huggingface.co/Liyux/pusht_diffusion_v5) | ResNet18 + UNet | DP baseline, 255 episodes (best) |
| [pusht_sfp_v9](https://huggingface.co/Liyux/pusht_sfp_v9) | ResNet18 + UNet | SFP working baseline |
| [pusht_sfp_v14](https://huggingface.co/Liyux/pusht_sfp_v14) | ResNet18 + UNet | SFP with h50/k2/σ1 params |
| [pusht_sfp_v15](https://huggingface.co/Liyux/pusht_sfp_v15) | ResNet18 + MLP | SFP with cond_residual MLP (RRAM-compatible) |

## Dataset

| Dataset | Episodes | Description |
|---------|----------|-------------|
| [pusht_real_merged](https://huggingface.co/datasets/Liyux/pusht_real_merged) | 255 | Real robot Push-T task, SO-101 arm, 320x240 |

## Key Results

**Sim (2D Push-T):**
- MLP achieves 0.86-0.88 FP32 vs UNet 0.74
- INT8 quantization: Bottleneck128+Skip achieves 0.86
- Noise tolerance: <6% accuracy drop at 10% multiplicative noise

**Real Robot:**
- DP v5: >90% success rate
- SFP v9: >70% success rate
- SFP v14/v15: > 80% success rate

## Code

- [Liyux3/lerobot_MLP-SFP](https://github.com/Liyux3/lerobot_MLP-SFP)

## Hardware

- Robot: SO-101 (LeRobot compatible)
- Camera: HB Camera, top-down, 320x240 @ 30fps
- Training: 2x RTX 4090

## Citation

Coming soon.

## Acknowledgments

- [LeRobot](https://github.com/huggingface/lerobot) framework
- [Streaming Flow Policy](https://arxiv.org/abs/2505.21851) paper
- HKU EEE

---

*Part of FYP project at The University of Hong Kong, supervised by Prof. Han Wang.*