gym-pusht

A gymnasium environment PushT.

Installation

Create a virtual environment with Python 3.10 and activate it, e.g. with miniconda:

conda create -y -n pusht python=3.10 && conda activate pusht

Install gym-pusht:

pip install gym-pusht

Quick start

# example.py
import gymnasium as gym
import gym_pusht

env = gym.make("gym_pusht/PushT-v0", render_mode="human")
observation, info = env.reset()

for _ in range(1000):
    action = env.action_space.sample()
    observation, reward, terminated, truncated, info = env.step(action)
    image = env.render()

    if terminated or truncated:
        observation, info = env.reset()

env.close()

Description

PushT environment.

The goal of the agent is to push the block to the goal zone. The agent is a circle and the block is a tee shape.

Action Space

The action space is continuous and consists of two values: [x, y]. The values are in the range [0, 512] and represent the target position of the agent.

Observation Space

If obs_type is set to state, the observation space is a 5-dimensional vector representing the state of the environment: [agent_x, agent_y, block_x, block_y, block_angle]. The values are in the range [0, 512] for the agent and block positions and [0, 2*pi] for the block angle.

If obs_type is set to environment_state_agent_pos the observation space is a dictionary with: - environment_state: 16-dimensional vector representing the keypoint locations of the T (in [x0, y0, x1, y1, ...] format). The values are in the range [0, 512]. - agent_pos: A 2-dimensional vector representing the position of the robot end-effector.

If obs_type is set to pixels, the observation space is a 96x96 RGB image of the environment.

Rewards

The reward is the coverage of the block in the goal zone. The reward is 1.0 if the block is fully in the goal zone.

Success Criteria

The environment is considered solved if the block is at least 95% in the goal zone.

Starting State

The agent starts at a random position and the block starts at a random position and angle.

Episode Termination

The episode terminates when the block is at least 95% in the goal zone.

Arguments

>>> import gymnasium as gym
>>> import gym_pusht
>>> env = gym.make("gym_pusht/PushT-v0", obs_type="state", render_mode="rgb_array")
>>> env
<TimeLimit<OrderEnforcing<PassiveEnvChecker<PushTEnv<gym_pusht/PushT-v0>>>>>

• obs_type: (str) The observation type. Can be either state, environment_state_agent_pos, pixels or pixels_agent_pos. Default is state.

• block_cog: (tuple) The center of gravity of the block if different from the center of mass. Default is None.

• damping: (float) The damping factor of the environment if different from 0. Default is None.

• render_mode: (str) The rendering mode. Can be either human or rgb_array. Default is rgb_array.

• observation_width: (int) The width of the observed image. Default is 96.

• observation_height: (int) The height of the observed image. Default is 96.

• visualization_width: (int) The width of the visualized image. Default is 680.

• visualization_height: (int) The height of the visualized image. Default is 680.

Reset Arguments

Passing the option options["reset_to_state"] will reset the environment to a specific state.

>>> import gymnasium as gym
>>> import gym_pusht
>>> env = gym.make("gym_pusht/PushT-v0")
>>> state, _ = env.reset(options={"reset_to_state": [0.0, 10.0, 20.0, 30.0, 1.0]})
>>> state
array([ 0.      , 10.      , 57.866196, 50.686398,  1.      ],
        dtype=float32)

Version History

• v0: Original version

References

• TODO:

Contribute

Instead of using pip directly, we use poetry for development purposes to easily track our dependencies. If you don't have it already, follow the instructions to install it.

Install the project with dev dependencies:

poetry install --all-extras

Follow our style

# install pre-commit hooks
pre-commit install

# apply style and linter checks on staged files
pre-commit

Acknowledgment

gym-pusht is adapted from Diffusion Policy