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Ag2Manip

Code Repository for Ag2Manip: Learning Novel Manipulation Skills with Agent-Agnostic Visual and Action Representations

by Puhao Li *, Tengyu Liu *, Yuyang Li, Muzhi Han, Haoran Geng, Shu Wang, Yixin Zhu, Song-Chun Zhu, Siyuan Huang

Paper PDF Paper arXiv Project Page

Teaser Ag2Manip enables various manipulation tasks in scenarios where domain-specific demonstrations are unavailable. With agent-agnostic visual and action representations, Ag2Manip: (a) learns from human manipulation videos; (b) acquires diverse manipulation skills autonomously in simulation; and (c) supports robust imitation learning of manipulation skills in the real world.

Abstract

Enhancing the ability of robotic systems to autonomously acquire novel manipulation skills is vital for applications ranging from assembly lines to service robots. Existing methods (e.g., VIP, R3M) rely on learning a generalized representation for manipulation tasks but overlook (i) the domain gap between distinct embodiments and (ii) the sparseness of successful task trajectories within the embodiment-specific action space, leading to misaligned and ambiguous task representations with inferior learning efficiency. Our work addresses the above challenges by introducing Ag2Manip (Agent-Agnostic representations for Manipulation) for learning novel manipulation skills. Our approach encompasses two principal innovations: (i) a novel agent-agnostic visual representation trained on human manipulation videos with embodiments masked to ensure generalizability, and (ii) an agent-agnostic action representation that abstracts the robot’s kinematic chain into an agent proxy with a universally applicable action space to focus on the core interaction between the end-effector and the object. Through our experiments, Ag2Manip demonstrates remarkable improvements across a diverse array of manipulation tasks without necessitating domain-specific demonstrations, substantiating a significant 325% improvement in average success rate across 24 tasks from FrankaKitchen, ManiSkill, and PartManip. Further ablation studies underscore the critical role of both representations in achieving such improvements.

Pipeline

We first process a human demonstration dataset by masking and inpainting the areas occupied by human. Then we train an agent-agnostic visual representation on this dataset. We harness RL to learn manipulation policies in an agent-agnostic action space that abstracts the end-effector into an agent proxy, with a novel reward function emerging from our agent-agnostic visual representation. Lastly, the trajectory devised for the proxy agent is adapted to the robot through Inverse Kinematics.

Installation

  1. Create a new conda environment and activate it.

    conda create -n ag2manip python=3.8
conda activate ag2manip

  2. Install dependent libraries with pip.

    pip install torch==1.13.1+cu117 torchvision==0.14.1+cu117 --extra-index-url https://download.pytorch.org/whl/cu117
pip install -r requirements.txt
    • The code is tested on pytorch1.13.1 and cuda11.7, modify the installation command to install other versions of pytorch.

  3. Install Isaac Gym by following the official documentation.

Data preparation

We choose Epic-Kitchen as the human demonstration dataset. First, segment the human body from each frame using the ODISE algorithm. Then employ a video inpainting model, E2FGVI, to fill in the areas previously occupied by the human.

IsaacGym Assets

To access the assets for the simulated environments, please head to Google Drive.

Usage

Train Visual Representation

  1. Train our visual representation model on EPIC-KITCHEN dataset: 
    • cd repre_trainer
    • Run train_ddp.py to train our model on multiple GPUs in parallel, or run train.py to train on a single GPU.
  2. Specify your model save path by modifying exp_name in repre_trainer/cfgs/default.yml.
  3. You can access the pre-trained ag2manip visual representation model checkpoints here.

Train Manipulation Skills with a Proxy Agent

python train.py
    --plan: store_true, run transfered trajectory, no training.
    --traj_path: str, dummy trajectory path for planning.
    --save_goal: store_true, rather save goal image.
    --save_video: store_true, rather save executed video.
    --agentago: store_true, make video agent-agnostic.
    --seed: int, global random seed(default: 42).
    --save_traj: store_true, run trained policy, no training, save the test result.
    --task: str, specific the task in the environment (args.env) to run.
    --camera: str, specific the camera sensor in the environment to run.
    --disable_wandb: store_true, disable wandb logging.
    --debug_vis: store_true, enable debug visualization.
    --randomize: store_true, apply env reset randomization.
    --test: store_true, run trained policy, no training.
    --play: store_true, run trained policy, the same as test, can be used only by rl_games RL library.
    --resume: int, resume training or start testing from a checkpoint.
    --checkpoint: str, path to the saved weights, only for rl_games RL library.
    --headless: store_true, force display off at all times.
    --logdir: str, log path.
    --num_envs: int, number of environments to create - override config file.
    --episode_length: int, episode length, by default is read from yaml config.
    --seed: int, random seed.
    --algo: str, choose an RL algorithm.
    --model_dir: str, choose a model dir.

For example, train a manipulation task task_name from benchmark_name (frankakitchen, partmanip, maniskill) with our method ag2manip in IsaacGym with the following command:

python train.py --task=benchmark_name@task_name@ag2manip --algo=ppo --seed=42 --cfg_train=cfgs/algo/ppo/manipulation.yaml --disable_wandb --camera=default

The best policy will be saved as model_best.pt in logs/ag2manip/task_name@default/ag2manip@ppo.42/.

Inference with a Proxy Agent

Inference and save a trajectory generated by the trained policy logs/ag2manip/task_name@default/ag2manip@ppo.42/model_best.pt:

python train.py --task=benchmark_name@task_name@ag2manip --model_dir=logs/ag2manip/task_name@default/ag2manip@ppo.42/model_best.pt --test --save_traj --algo=ppo --cfg_train=cfgs/algo/ppo/manipulation.yaml --camera=default --seed=0 --disable_wandb

The trajectory will be saved as logs/ag2manip/task_name@default/ag2manip@ppo.42/absres_best.pkl.

Plan with a Franka Robot Arm

Plan with a franka robot arm using the saved trajectory:

python plan.py --task=benchmark_name@task_name@ag2manip --traj_path=logs/ag2manip/task_name@default/ag2manip@ppo.42/absres_best.pkl --pipeline=cpu --algo=ppo --cfg_train=cfgs/algo/ppo/manipulation.yaml --disable_wandb --camera=default

Citation

If you find this work is helpful, please consider citing us as

@article{li2024ag2manip,
  title={Ag2Manip: Learning Novel Manipulation Skills with Agent-Agnostic Visual and Action Representations},
  author={Li, Puhao and Liu, Tengyu and Li, Yuyang and Han, Muzhi and Geng, Haoran and Wang, Shu and Zhu, Yixin and Zhu, Song-Chun and Huang, Siyuan},
  journal={arXiv preprint arXiv:2404.17521},
  year={2024}
}

Contact

If you have any questions about this work, feel free to contact Puhao Li at puhaoli01@gmail.com.