ManiCM: Real-time 3D Diffusion Policy via Consistency Model for Robotic Manipulation

Project Page | arXiv | Paper

Guanxing Lu*, Zifeng Gao*, Tianxing Chen, Wenxun Dai, Ziwei Wang, Yansong Tang^†

ManiCM Overview: Given a raw action sequence a₀, we first perform a forward diffusion to introduce noise over n + k steps. The resulting noisy sequence a_n+k is then fed into both the online network and the teacher network to predict the clean action sequence. The target network uses the teacher network’s k-step estimation results to predict the action sequence. To enforce self-consistency, a loss function is applied to ensure that the outputs of the online network and the target network are consistent.

💻 Installation

See INSTALL.md for installation instructions.

📚 Config

Algorithms. We provide the implementation of the following algorithms:

• DP3: dp3.yaml
• ManiCM: dp3_cm.yaml

You can modify the configuration of the teacher model and ManiCM by editing these two files. Here are the meanings of some important configurations:

num_inference_timesteps: The inference steps of ManiCM.

num_train_timesteps: Total time step for adding noise.

prediction_type: epsilon represents prediction noise, while sample represents predicted action.

For more detailed arguments, please refer to the scripts and the code.

🛠️ Usage

Scripts for generating demonstrations, training, and evaluation are all provided in the scripts/ folder.

The results are logged by wandb, so you need to wandb login first to see the results and videos.

We provide a simple instruction for using the codebase here.

1. Generate demonstrations by gen_demonstration_adroit.sh and gen_demonstration_dexart.sh. See the scripts for details. For example:

   bash scripts/gen_demonstration_adroit.sh hammer

   This will generate demonstrations for the hammer task in Adroit environment. The data will be saved in ManiCM/data/ folder automatically.

2. Train and evaluate a teacher policy with behavior cloning. For example:

   # bash scripts/train_policy.sh config_name task_name addition_info seed gpu_id 
   bash scripts/train_policy.sh dp3 adroit_hammer 0603 0 0

   This will train a DP3 policy on the hammer task in Adroit environment using point cloud modality. By default we save the ckpt (optional in the script). During training, teacher's model takes ~10G gpu memory and ~4 hours on an Nvidia 4090 GPU.

3. Move teacher's ckpt. For example:

   # bash scopy.sh alg_name task_name teacher_addition_info addition_info seed gpu_id
   bash scopy.sh dp3_cm adroit_hammer 0603 0603_cm 0 0

4. Train and evaluate ManiCM. For example:

   # bash scripts/train_policy_cm.sh config_name task_name addition_info seed gpu_id
   bash scripts/train_policy_cm.sh dp3_cm adroit_hammer 0603_cm 0 0

   This will train ManiCM use a DP3 policy teacher model on the hammer task in Adroit environment using point cloud modality. During training, ManiCM model takes ~10G gpu memory and ~4 hours on an Nvidia 4090 GPU.

🏞️ Checkpoints

We have updated the pre-trained checkpoints of hammer task in Adroit environment for your convenience. You can download them and place the folder into data/outputs/.

🏷️ License

This repository is released under the MIT license.

🙏 Acknowledgement

Our code is built upon 3D Diffusion Policy, MotionLCM, Latent Consistency Model, Diffusion Policy, VRL3, Metaworld, and ManiGaussian. We would like to thank the authors for their excellent works.

🥰 Citation

If you find this repository helpful, please consider citing:

@article{lu2024manicm,
      title={ManiCM: Real-time 3D Diffusion Policy via Consistency Model for Robotic Manipulation}, 
      author={Guanxing Lu and Zifeng Gao and Tianxing Chen and Wenxun Dai and Ziwei Wang and Yansong Tang},
      journal={arXiv preprint arXiv:2406.01586},
      year={2024}
}