README.md

metadata

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	ResNet18 + UNet	DP baseline, 136 episodes
pusht_diffusion_v4	ResNet18 + UNet	DP baseline, 226 episodes
pusht_diffusion_v5	ResNet18 + UNet	DP baseline, 255 episodes (best)
pusht_sfp_v9	ResNet18 + UNet	SFP working baseline
pusht_sfp_v14	ResNet18 + UNet	SFP with h50/k2/σ1 params
pusht_sfp_v15	ResNet18 + MLP	SFP with cond_residual MLP (RRAM-compatible)

Dataset

Dataset	Episodes	Description
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

Hardware

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

Citation

Coming soon.

Acknowledgments

• LeRobot framework
• Streaming Flow Policy paper
• HKU EEE

---

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