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Diverse Controllable Diffusion Policy with Signal Temporal Logic

Journal Conference

Project page

Reliable Autonomous Systems Lab @ MIT (REALM)

Yue Meng, Chuchu Fan

Official implementation for RA-L2024 / ICRA2025 paper, "Diverse Controllable Diffusion Policy with Signal Temporal Logic." [link]

  • A GenAI framework to learn diverse and controllable policy for autonomous driving to satisfy Signal Temporal Logic (STL) specifications.
  • Implemented NuScenes data visualizer and annotation tool (with annotated data) [here]

image info

Normal scenario Steer to avoid collision Change lane to avoid collision 
@article{meng2024diverse,
  title={Diverse Controllable Diffusion Policy with Signal Temporal Logic},
  author={Meng, Yue and Fan, Chuchu},
  journal={IEEE Robotics and Automation Letters},
  year={2024},
  publisher={IEEE}
}

Pre-requisite

Ubuntu 22.04 / 24.04 with GPU (V100; RTX2080Ti / 3090Ti / 4090)

  1. Download the NuScenes dataset (follow instructions on NuScenes website)

  2. Set MY_DATA_DIR environment variable to the path that contains the nuscenes folder

  3. Install python packages (via Conda)

conda create -n pstl_env python=3.7.6
conda activate pstl_env
conda install pytorch==1.13.1 pytorch-cuda=11.7 matplotlib imageio scikit-learn -c pytorch -c nvidia
  1. Fetch the data and pretrained models from the google drive.

(Optional) Data preparation and train the model from scratch

You can skip this section if use the data and pretrained models from the google drive.

Data collection

python nusc_train.py -e e0_nusc_cache --collect_data --num_workers 4

Trajectory optimization augmented dataset

python nusc_train.py -e e1_nusc_trajopt --trajopt_only

Our methods

Ours

# step-1, train DDPM (with data augmentation)
python nusc_train.py -e e5_ddpm --diffusion --stl_weight 0.0 --load_stlp
# step-2, train RefineNet based on DDPM
python nusc_train.py -e e7_ours --diffusion --stl_weight 0.0 --load_stlp -P e5_ddpm --rect_head --flex --diverse_loss --multi_cands 5

Ablations

Ours (w/o RefineNet) uses DDPM (with data augmentation), which is trained in "Ours" section above

Ours ($\mathcal{L}_{STL}$)

python nusc_train.py -e e8_ours_ablation --diffusion --stl_weight 1.0 --load_stlp --load_tj --rect_head --flex -P e5_ddpm --diversity_weight 0.0 --n_shards 4 --interval --multi_cands 5 --diff_full

Baselines

VAE

python nusc_train.py -e e2_vae_mono --bc_weight 1.0 --stl_weight 0.0 --load_stlp --vae --flex --gt_data_training

VAE (with data augmentation)

python nusc_train.py -e e3_vae --bc_weight 1.0 --stl_weight 0.0 --load_stlp --vae --flex --use_init_hint

DDPM

python nusc_train.py -e e4_ddpm_mono --diffusion --stl_weight 0.0 --load_stlp --gt_data_training

* DDPM (with data augmentation) is trained in "Ours" section

TrafficSim

python nusc_train.py -e e6_trafficsim --bc_weight 1.0 --stl_weight 1.0 --load_stlp --vae --flex --collision_loss 1.0

* CTG leverages DDPM model (with data augmentation), which is trained in "Ours" section

Open-loop test on NuScenes (Table I)

Our methods

Ours

python nusc_train.py -e e7_ours --diffusion --stl_weight 0.0 --load_stlp --rect_head --flex --diverse_loss --multi_cands 5 --test -P e7_ours --run_sampling_test --skip_nusc_load --viz_correct

Ours+guidance

python nusc_train.py -e e7_ours --diffusion --stl_weight 0.0 --load_stlp --rect_head --flex --diverse_loss --multi_cands 10 --test -P e7_ours --run_sampling_test  --viz_correct --guidance --guidance_before 10 --guidance_niters 1 --guidance_lr 0.01 --n_rolls 3 --other --skip_nusc_load

Ablations

Ours (w/o RefineNet)

python nusc_train.py -e e7_ours --diffusion --stl_weight 0.0 --load_stlp --rect_head --flex --diverse_loss --multi_cands 5 --run_sampling_test --test -P e7_ours --skip_nusc_load --viz_correct --no_refinenet

Ours ($\mathcal{L}_{STL}$)

python nusc_train.py -e e8_ours_ablation --diffusion --stl_weight 1.0 --load_stlp --rect_head --flex --diversity_weight 0.0 --multi_cands 5 --run_sampling_test --test -P e8_ours_ablation --skip_nusc_load --viz_correct

Baselines

VAE

python nusc_train.py -e e2_vae_mono --bc_weight 1.0 --stl_weight 0.0 --load_stlp --vae --flex --run_sampling_test --test -P e2_vae_mono --skip_nusc_load --viz_correct

VAE (with data augmentation)

python nusc_train.py -e e3_vae --bc_weight 1.0 --stl_weight 0.0 --load_stlp --vae --flex --use_init_hint --run_sampling_test --test -P e3_vae --skip_nusc_load --viz_correct

DDPM

python nusc_train.py -e e4_ddpm_mono --diffusion --stl_weight 0.0 --load_stlp --flex --run_sampling_test --test -P e4_ddpm_mono --skip_nusc_load --viz_correct

DDPM (with data augmentation)

python nusc_train.py -e e5_ddpm --diffusion --stl_weight 0.0 --load_stlp --flex --run_sampling_test --test -P e5_ddpm --skip_nusc_load --viz_correct

TrafficSim

python nusc_train.py -e e6_trafficsim --bc_weight 1.0 --stl_weight 1.0 --load_stlp --flex --vae --collision_loss 1.0 --run_sampling_test --test -P e6_trafficsim --other --viz_correct --skip_nusc_load

CTG

python nusc_train.py -e e5_ddpm --diffusion --stl_weight 0.0 --load_stlp --flex --run_sampling_test --test -P e5_ddpm --skip_nusc_load --viz_correct --guidance

Closed-loop test on NuScenes (Table II)

Our methods

Ours

python nusc_sim.py -e e7_ours --diffusion --stl_weight 0.0 --rect_head --flex --diverse_loss --multi_cands 5 --test -P e7_ours --filter_traj 0 --test_scenes --viz_last --suffix sim

Ours+guidance

python nusc_sim.py -e e7_ours --diffusion --stl_weight 0.0 --rect_head --flex --diverse_loss --multi_cands 5 --test -P e7_ours --filter_traj 0 --test_scenes --viz_last --guidance --guidance_before 10 --guidance_niters 1 --guidance_lr 0.04 --suffix sim_guide

Generate videos

python nusc_sim.py -e e7_ours --diffusion --stl_weight 0.0 --rect_head --flex --diverse_loss --multi_cands 5 --test -P e7_ours --filter_traj 0 --test_scenes --guidance --guidance_before 10 --guidance_niters 1 --guidance_lr 0.04 --suffix video

Baselines

VAE (with data augmentation)

python nusc_sim.py -e e3_vae --vae --stl_weight 0.0 --use_init_hint --bc_weight 1.0 --flex --test -P e3_vae --filter_traj 0 --test_scenes --viz_last --suffix sim

DDPM (with data augmentation)

python nusc_sim.py -e e5_ddpm --diffusion --stl_weight 0.0 --flex --test -P e5_ddpm --filter_traj 0 --test_scenes --viz_last --suffix sim

TrafficSim

python nusc_sim.py -e e6_trafficsim --bc_weight 1.0 --stl_weight 1.0 --flex --vae --collision_loss 1.0 --test -P e6_trafficsim --filter_traj 0 --test_scenes --viz_last --suffix sim

CTG

python nusc_sim.py -e e5_ddpm --diffusion --stl_weight 0.0 --flex --test -P e5_ddpm --guidance --filter_traj 0 --test_scenes --viz_last --suffix sim_ctg