---
tags:
- robotics
- diffusion-policy
- drone-navigation
- obstacle-avoidance
- imitation-learning
- pytorch
- isaac-lab
library_name: pytorch
---

# SDPC Diffusion Policy — Quadrotor Corridor Navigation

Trained checkpoint for **SDPC (Safe Diffusion Policy with Constraint)**, an image-(and goal-)conditioned
trajectory diffusion policy for quadrotor obstacle avoidance, with hard constraints enforced at
inference time via an SLSQP projection step. Developed as part of a master's thesis at Paderborn
University.

Code: [ashiqlathief/dpcc-thesis1](https://github.com/ashiqlathief/dpcc-thesis1)

## What this checkpoint is

A single trained run — `H8_K20_Dmodels.ImagePoseCondUNet1DTemporalCondModel_Evitp_L384`, seed `7` —
i.e. the `ImagePoseCondUNet1DTemporalCondModel` denoiser (UNet + ViT-P image encoder, goal/pose
conditioned). It predicts an 8-step action-chunk trajectory (`horizon=8`) via a 20-step DDPM
denoising process (`n_diffusion_steps=20`), conditioned on 1 past FPV observation (`n_obs_steps=1`)
and the relative goal vector.

The policy was trained on FPV-camera demonstrations of a quadrotor navigating a corridor around
static cylinder obstacles in NVIDIA Isaac Lab, collected with a cascaded-PID controller (see the
`isaac/scripts/quadcopter.py` data-collection script in the repo above). It is deployed either
open-loop (plain diffusion sampling) or with an in-the-loop SLSQP projection step that hard-enforces
obstacle avoidance and corridor bounds at every denoising step — see the repo's README ("How SDPC
works") for that mechanism.

## Model architecture

| Component | Value |
|---|---|
| Denoiser | `ImagePoseCondUNet1DTemporalCondModel` (1D temporal UNet, `dim=32`, `dim_mults=(1,2,4,8)`) |
| Image encoder | ViT-P (`vit_img_size=96`, `vit_patch_size=8`, `vit_width=512`, `vit_depth=6`, `vit_heads=8`) |
| Image conditioning dim | 384 |
| Goal/pose conditioning dim | 64 (relative goal vector `goal_rel`) |
| Action horizon | 8 |
| Observation steps | 1 |
| Diffusion steps (train/inference) | 20 |
| Predicts | `epsilon` (noise), `l2` loss |
| Classifier-free guidance | `condition_dropout=0.25`, `condition_guidance_w=1.2` |

## Training data

- Simulator: NVIDIA Isaac Lab (Isaac Sim)
- Task: navigate a straight corridor around 5 static cylindrical obstacles to a target position
- Observation: single FPV RGB image (96×96) + robot pose
- Action: relative position delta per control step
- Demonstrations: PID-controller autopilot trajectories, recorded via `isaac/scripts/quadcopter.py`
- Normalization: `LimitsNormalizer` (min/max per-dimension), embedded in this checkpoint so it can
be evaluated without the original dataset present

## Training procedure

| Hyperparameter | Value |
|---|---|
| Batch size | 8 |
| Learning rate | 1e-4 (Adam) |
| Gradient accumulation | 2 |
| Training steps | 100,000 |
| EMA decay | 0.995 |
| Train/test split | 0.9 |
| Loss | L2 on predicted noise, action-weighted (`action_weight=10`) |

## Files

| File | Purpose |
|---|---|
| `state_best.pt` | Model + EMA weights at the best validation checkpoint |
| `model_config.pkl` | Denoiser architecture config (`diffuser.utils.Config`) |
| `diffusion_config.pkl` | `GaussianDiffusion` wrapper config |
| `dataset_config.pkl` | Dataset/normalizer config used at train time |
| `trainer_config.pkl` | Optimizer/training-loop config |
| `losses.pkl` | Training loss curve |

## How to use

This checkpoint is meant to be loaded with the training/eval code in
[ashiqlathief/dpcc-thesis1](https://github.com/ashiqlathief/dpcc-thesis1), not standalone —
`model_config.pkl`/`diffusion_config.pkl` reference model classes defined in that repo's
`diffuser/models/` package.

```bash
# from the dpcc-thesis1 repo root, with env_isaaclab active
hf download ashiqali98/SDPC_diffusionmodel \
--local-dir isaac/logs/avoiding-crazyflie/diffusion/H8_K20_Dmodels.ImagePoseCondUNet1DTemporalCondModel_Evitp_L384/7

python scripts/eval_craziefliepos.py # RUN_DIR already defaults to the path above
```

```python
import diffuser.utils as utils

RUN_DIR = "isaac/logs/avoiding-crazyflie/diffusion/H8_K20_Dmodels.ImagePoseCondUNet1DTemporalCondModel_Evitp_L384/7"
diff_exp = utils.load_diffusion(RUN_DIR, epoch="best", device="cuda:0")
diffusion = diff_exp.diffusion.eval()
```

## Limitations

- Trained and evaluated entirely in simulation on a fixed, small set of static obstacle layouts —
not validated for arbitrary real-world scenes.
- No dynamic-obstacle-avoidance guarantee: obstacle positions used at inference are either
ground-truth (simulation) or perceived via a depth-camera pipeline with known accuracy limits
(see the repo's `depth_obstacle_estimator.py` and its documented bug history).
- Quantitative success-rate / constraint-violation results across projection variants are reported
in the accompanying thesis, not restated here to avoid duplicating numbers that may be revised
before submission.