Diffusion Transformer Policy

Please refer to the extension repo:

Dita:Scaling Diffusion Transformer for Generalist Vision-Language-Action Policy

Installation

To run the code, you should install the requiresments. The code is run on python3.10 and pytorch 2.2.0, tensorflow==2.15.0, CUDA 12.1.

 pip install -r requirements.txt

Then, clone the code as follow,

git clone https://github.com/zhihou7/dit_policy

Model Checkpoints

We provide the corresponding models, that can be utilized for finetuing.

Model	Description	Checkpoint Path
DiT Policy	Diffusion Transformer Policy	Google Drive
DiT Policy	Diffusion Transformer Policy (w/o image augmentation)	Google Drive
Diffusion MLP Head	Transformer with Diffusion Head Policy (w/o image augmentation)	Google Drive

Training & Finetuning

PRETRAINING on OXE dataset

Before you run the code, you should update the s3 key "AWS_ACCESS_KEY_ID", "AWS_SECRET_ACCESS_KEY", "S3_ENDPOINT". We train the network with 32 GPUs.

python scripts/train_diffusion_oxe.py task_name=openx_full_train_o2_p32 dataset.traj_length=32 num_pred_action=31 scheduler_type=1 shuffle_buffer_size=128000 dataname=oxe_magic_soup_plus task_name=oxe_full_train_o2_p32_wotimestep_oxe_noclamp_filter batch_size=256 

We observe that image augmentation is beneficial for SimplerEnv in our experiments. If you want to use image augmentation, please add ``+image_aug=1''

Finetuning with Lora

Here, we provide an example for finetuning with lora, i.e., the 10-shot finetuning code on Real-Franka Arm.

python3 scripts/finetune_realdata.py +pretrained_path=dit_policy_checkpoint.pth dataset.traj_per_episode=16 dataset.traj_length=1 task_name=new_test_nodiffhead_few10_250124 num_pred_action=1 dataname=lab_907_1 batch_size=32 dataset.train_data_list=you pkl dataname file to include the collected pkl files name use_lora=True scheduler_type=0 dataset.num_given_observation=1  max_iters=10000

scheduler_type=0 indicates we use 100 DDPM training steps.

Fully Finetuning on CALVIN

At first, you should follow the instruction-calvin to install CALVIN environment.

we train the network with 4GPUs.

python scripts/train_diffusion_sim.py --config-name config_diffusion_calvin batch_size=32 dataset.traj_length=11 num_pred_action=10 task_name=calvin_exp dataset.num_given_observation=2 dataset=fix_camera use_close_loop_eval=True close_loop_eval.test_episodes_num=32 dataset.use_baseframe_action=True taskname=task_ABC_D dataname=calvin_mc close_loop_eval.eval_iters=10000 close_loop_eval.test_episodes_num=250 scheduler_type=0 wrap_grmg_data=2 +pretrained_path=dit_policy_checkpoint.pth +use_adjust_scheduler=true lr=0.0001 epoch=15 +min_lr_scale=0.01 scheduler.warmup_epochs=1

Simulation Benchmark Evaluations

LIBERO Simulation Benchmark Evaluations

Method	LIBERO-Spatial	LIBERO-Object	LIBERO-Goal	LIBERO-Long	Average
Diffusion Policy from scratch	78.3	92.5%	68.3 %	50.5 %	72.4 %
Octo fine-tuned	78.9 %	85.7 %	84.6%	51.1 %	75.1 %
OpenVLA fine-tuned	84.7 %	88.4 %	79.2 %	53.7 %	76.5 %
ours fine-tuned	84.2%	96.3%	85.4%	63.8%	82.4%

Calvin (ABC->D)

Method	Input	1	2	3	4	5	Avg.Len.
RoboFlamingo	S-RGB, G-RGB	82.4%	61.9%	46.6%	33.1%	23.5%	2.47
SuSIE	S-RGB	87.0%	69.0%	49.0%	38.0%	26.0%	2.69
GR-1	S-RGB, G-RGB, P	85.4%	71.2%	59.6%	49.7%	40.1%	3.06
3D Diffuser	S-RGBD, G-RGBD, Proprio, Cam	92.2%	78.7%	63.9%	51.2%	41.2%	3.27
ours w/o pretraining	Static-RGB	89.5%	63.3%	39.8%	27.3%	18.5%	2.38
ours	Static-RGB	94.5%	82.5%	72.8%	61.3%	50.0%	3.61

Simulation Benchmark Evaluations

SimplerEnv

This evaluation is based on SimplerEnv

	0	3	4	5	6	8
coke_can/matching_avg	0.7266666666666669	0.567	0.787	0.17	nan	0.163
coke_can/variant_avg	0.6	0.49	0.823	0.006	nan	0.545
coke_can/matching/horizontal	0.8500000000000001	0.82	0.74	0.21	nan	0.27
coke_can/matching/vertical	0.7400000000000001	0.33	0.74	0.21	nan	0.03
coke_can/matching/standing	0.5900000000000001	0.55	0.88	0.09	nan	0.19
coke_can/variant/horizontal	0.6799999999999999	0.569	0.822	0.005	nan	0.711
coke_can/variant/vertical	0.5066666666666667	0.204	0.754	0.0	nan	0.271
coke_can/variant/standing	0.6133333333333334	0.698	0.893	0.013	nan	0.653
move_near/variant	0.5213089271066149	0.323	0.792	0.031	nan	0.477
move_near/matching	0.49126940133037694	0.317	0.779	0.042	nan	0.462
drawer/matching_avg	0.4629629629629629	0.597	0.25	0.227	nan	0.356
drawer/variant_avg	0.3752343844338537	0.294	0.353	0.011	nan	0.177
drawer/matching/open	0.2314814814814815	0.296	0.157	0.009	nan	0.194
drawer/matching/close	0.6944444444444443	0.891	0.343	0.444	nan	0.518
drawer/variant/open	0.2155516441230727	0.069	0.333	0.0	nan	0.158
drawer/variant/close	0.5349171247446347	0.519	0.372	0.021	nan	0.195
put_spoon_on_tablecloth/matching_partial	0.25	0.167	nan	0.347	0.778	0.041
put_spoon_on_tablecloth/matching_entire	0.16666666666666666	0.0	nan	0.125	0.472	0.0
put_carrot_on_plate/matching_partial	0.20833333333333334	0.208	nan	0.528	0.278	0.333
put_carrot_on_plate/matching_entire	0.16666666666666666	0.042	nan	0.083	0.097	0.0
stack_green_block_on_yellow_block/matching_partial	0.08333333333333333	0.083	nan	0.319	0.403	0.125
stack_green_block_on_yellow_block/matching_entire	0.0	0.0	nan	0.0	0.042	0.0
put_eggplant_in_basket/matching_partial	0.08333333333333333	0.0	nan	0.667	0.875	0.083
put_eggplant_in_basket/matching_entire	0.0	0.0	nan	0.431	0.569	0.041
apple_in_drawer/matching_avg	0.04203703703703703	0.213	0.037	0.0	0.0	nan
apple_in_drawer/variant_avg	0.035355068856811014	0.101	0.206	0.0	0.0	nan
models	ours	RT-1-X	RT-2-X	Octo-Base	Octo-Small	OpenVLA

In our experiments, we use the Bridge_orig from tfds in google cloud, in which the image has been resized (480*512->224*224) and caused image distortion. We think this part might significantly affect the evaluation on bridige. Please notice that RT-2-X is a huge model with web-scale data.

Real Franka Demonstration

Please refer to the project page.

Acknowledgement

The dataloader code of OXE is based on OpenVLA, The dataloader code of CALVIN is based on GR-MG, The architecture is based on transformers.

Citation

If you find our code or models useful in your work, please cite our paper:

@article{hou2024diffusion,
  title={Diffusion Transformer Policy},
  author={Hou, Zhi and Zhang, Tianyi and Xiong, Yuwen and Pu, Hengjun and Zhao, Chengyang and Tong, Ronglei and Qiao, Yu and Dai, Jifeng and Chen, Yuntao},
  journal={arXiv preprint arXiv:2410.15959},
  year={2024}
}