trifinger-rl-datasets 0.9.2

Gym environments which provide offline RL datasets collected on the TriFinger system.

TriFinger RL Datasets

This repository provides offline reinforcement learning datasets collected on the TriFinger platform (simulated or real). The paper "Benchmarking Offline Reinforcement Learning on Real-Robot Hardware" (TODO: Link) discusses the datasets and benchmarks offline reinforcement learning methods on them. The code for loading the datasets follows the interface suggested by D4RL.

TODO: Link to documentation. TODO: Prominently mention (and repeat later on) that the repository also provides versions of the datasets with image observations from three cameras.

Some of the datasets were used during the Real Robot Challenge 2022.

Installation

To install the package run with python 3.8 in the root directory of the repository (we recommend doing this in a virtual environment):

pip install --upgrade pip  # make sure recent version of pip is installed
pip install .

Usage

Loading the dataset

The datasets are accessible via gym environments which are automatically registered when importing the package. They are automatically downloaded when requested and stored in ~/.trifinger_rl_datasets as HDF5 files.

The datasets are named following the pattern trifinger-cube-task-source-quality-v0 where task is either push or lift, source is either sim or real and quality can be either mixed or expert.

By default the observations are loaded as flat arrays. For the simulated datasets the environment can be stepped and visualized. Example usage (also see demo/load_dataset.py):

import gymnasium as gym

import trifinger_rl_datasets

env = gym.make(
    "trifinger-cube-push-sim-expert-v0",
    disable_env_checker=True,
    visualization=True,  # enable visualization
)

dataset = env.get_dataset()

print("First observation: ", dataset["observations"][0])
print("First action: ", dataset["actions"][0])
print("First reward: ", dataset["rewards"][0])

obs = env.reset()
done = False
while not done:
    obs, rew, done, info = env.step(env.action_space.sample())

Alternatively, the observations can be obtained as nested dictionaries. This simplifies working with the data. As some parts of the observations might be more useful than others, it is also possible to filter the observations when requesting dictionaries (see demo/load_filtered_dicts.py):

    # Nested dictionary defines which observations to keep.
    # Everything that is not included or has value False
    # will be dropped.
    obs_to_keep = {
        "robot_observation": {
            "position": True,
            "velocity": True,
            "fingertip_force": False,
        },
        "object_observation": {"keypoints": True},
    }
    env = gym.make(
        args.env_name,
        disable_env_checker=True,
        # filter observations,
        obs_to_keep=obs_to_keep,
    )

To transform the observation back to a flat array after filtering, simply set the keyword argument flatten_obs to true. Note that the step and reset functions will transform observations in the same manner as the get_dataset method to ensure compatibility. A downside of working with observations in the form of dictionaries is that they cause a considerable memory overhead during dataset loading.

All datasets come in two versions: with and without camera observations. The versions with camera observations contain -image in their name. Despite PNG image compression they are more than one order of magnitude bigger than the imageless versions. To avoid running out of memory, a part of a dataset can be loaded by specifying a range of timesteps:

env = gym.make(
    "trifinger-cube-push-real-expert-image-v0",
    disable_env_checker=True
)

# load only a subset of obervations, actions and rewards
dataset = env.get_dataset(rng=(1000, 2000))

The camera observations corresponding to this range are then returned in dataset["images"] with the following dimensions:

n_timesteps, n_cameras, n_channels, height, width = dataset["images"].shape

Since the camera frequency is lower than the control frequency, a camera image will repeat over several time steps. To load an array of camera images without this redundancy, the get_image_data method can be used:

# images from 3 cameras for each timestep
image_stats = env.get_image_stats()
n_cameras = image_stats["n_cameras"]
images = env.get_image_data(rng=(0, n_cameras * n_camera_frames_to_load)) 

Evaluating a policy in simulation

This package contains an executable module trifinger_rl_datasets.evaluate_sim, which can be used to evaluate a policy in simulation. As arguments it expects the task ("push" or "lift") and a Python class that implements the policy, following the PolicyBase interface:

python3 -m trifinger_rl_datasets.evaluate_sim push my_package.MyPolicy

For more options see --help.