This package has been archived since the RRC 2022 is over. For future work consider using trifinger_rl_datasets instead, which covers a larger set of datasets.

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Real Robot Challenge III (2022) Datasets

This repository provides offline reinforcement learning datasets collected on the TriFinger platform (simulated or real). It follows the interface suggested by D4RL.

The datasets are used for the Real Robot Challenge 2022. See also the RRC 2022 Software Documentation.

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 ~/.rrc_2022_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 gym

import rrc_2022_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.

Evaluating a policy in simulation

This package contains an executable module rrc_2022_datasets.evaluate_pre_stage, 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 rrc_2022_datasets.evaluate_pre_stage push my_package.MyPolicy

For more options see --help.

This is also used for the evaluation of submissions in the pre-stage of the challenge (hence the name).