---
license: apache-2.0
tags:
- robotics
- imitation-learning
- diffusion
- flow-matching
- droid
---

# Gen2Act C35 Diffused Gripper

This repository contains the ep9 slim checkpoint for a Gen2Act policy trained
on DROID-derived robot demonstrations. The policy predicts an 8-step camera-
frame delta-action chunk from a source demonstration video and current robot
observations.

## Model

- Architecture: fused-query flow-matching DiT
- Visual backbone: DINOv2 ViT-B/14
- Action: 9D delta pose plus a diffused binary gripper dimension
- Inputs: 8 source frames, three-frame front and wrist observation histories,
  current front patch geometry, and camera-projection proprioception
- Checkpoint: `c35_diffuse_gripper_fulltrain_ep9_latest_slim.pt`

The checkpoint is slim: it contains model weights, config, epoch, and metrics,
but no optimizer state. Load it with the matching Gen2Act codebase and config.

## Training Notes

The model was trained for 10 epochs; this uploaded checkpoint is ep9. Its
training-side validation pose MAE was 0.03150 and pose RMSE was 0.06031 in the
normalized action-evaluation logging pipeline. These are not closed-loop robot
success metrics.

## Intended Use and Limitations

This is a research checkpoint, not a safety-certified robot controller. It
requires calibration-compatible DROID-style observations and should be tested
in simulation or with appropriate hardware safety controls before any physical
deployment.