Gym Env

This is an Env class that implements the same interface as gym.Env.

Usage

from internutopia.core.config import Config, SimConfig
from internutopia.core.gym_env import Env
from internutopia.core.util import has_display
from internutopia.macros import gm
from internutopia_extension import import_extensions
from internutopia_extension.configs.robots.h1 import (
    H1RobotCfg,
    h1_camera_cfg,
    h1_tp_camera_cfg,
    move_along_path_cfg,
    move_by_speed_cfg,
    rotate_cfg,
)
from internutopia_extension.configs.tasks import SingleInferenceTaskCfg

headless = False
if not has_display():
    headless = True

h1 = H1RobotCfg(
    position=(0.0, 0.0, 1.05),
    controllers=[
        move_by_speed_cfg,
        move_along_path_cfg,
        rotate_cfg,
    ],
    sensors=[
        h1_camera_cfg.update(name='camera', resolution=(320, 240), enable=False),
        h1_tp_camera_cfg.update(enable=False),
    ],
)

config = Config(
    simulator=SimConfig(physics_dt=1 / 240, rendering_dt=1 / 240, use_fabric=False, headless=headless),
    task_configs=[
        SingleInferenceTaskCfg(
            scene_asset_path=gm.ASSET_PATH + '/scenes/empty.usd',
            scene_scale=(0.01, 0.01, 0.01),
            robots=[h1],
        ),
        SingleInferenceTaskCfg(
            scene_asset_path=gm.ASSET_PATH + '/scenes/empty.usd',
            scene_scale=(0.01, 0.01, 0.01),
            robots=[h1],
        ),
    ],
)

print(config.model_dump_json(indent=4))

import_extensions()

env = Env(config)
obs, _ = env.reset()

path = [(1.0, 0.0, 0.0), (1.0, 1.0, 0.0), (3.0, 4.0, 0.0)]
i = 0

move_action = {move_along_path_cfg.name: [path]}

while env.simulation_app.is_running():
    i += 1
    action = move_action
    obs, reward, terminated, truncated, info = env.step(action=action)
    if i % 100 == 0:
        print(i)

env.close()

Env Reset

Unlike the standard gym.Env, the internutopia.core.gym_env.Env class provides a reset() method that does not reset the environment to its initial state.

In InternUtopia, task_configs is a list where each element is referred to as an episode.

The purpose of reset() is to load the next episode.

The return values of reset() are obs and info:

1. When there are episodes remaining, reset() returns the obs and task_config of the newly loaded episode.

2. When all episodes have been executed, reset() returns None and None.

You can break the while env.simulation_app.is_running() loop when info is None to stop the simulation.

Env Step

The env.step(action) runs one timestep of the environment’s dynamics using the actions.

There are five outputs from env.step(action):

1. obs: The observation of the robot in the env.

2. reward: The reward of the env.

3. terminated: Whether the episode has ended.

4. truncated: Not used yet.

5. info: Not used yet.

When the current episode ends, env.reset() is not called automatically. Its return values will all be None (i.e., the return from env.step(action) will be (None, None, None, None, None)). You must manually call env.reset() to load a new episode.