DeepMind Control Locomotion

EnvPool implements the ten example factories and all three Soccer walker types from dm_control==1.0.44, using mujoco==3.11.0. These Composer tasks are separate from DeepMind Control Suite, which implements suite.ALL_TASKS. The independent dm_control.manipulation task collection is not included here.

Scene construction, maze generation, terrain generation, motion capture loading, observations, stepping, rewards, and rendering run in C++. The official Python package is only an oracle for tests and documentation generation.

Registered tasks

Each task also has an alias of the form dm_control/locomotion/<upstream_factory_name>. The registry is generated from the pinned upstream example factories and soccer.WalkerType enumeration.

Tasks

EnvPool ID	Actions	Time limit	Task
DmcCmuHumanoidRunWalls-v1	56	30 s	Corridor with walls
DmcCmuHumanoidRunGaps-v1	56	30 s	Corridor with gaps
DmcCmuHumanoidGoToTarget-v1	56	30 s	Reach a target
DmcCmuHumanoidMazeForage-v1	56	30 s	Random maze foraging
DmcCmuHumanoidHeterogeneousForage-v1	56	25 s	Targets with positive and negative rewards
DmcRodentEscapeBowl-v1	38	20 s	Escape a generated terrain bowl
DmcRodentRunGaps-v1	38	30 s	Corridor with gaps
DmcRodentMazeForage-v1	38	30 s	Random maze foraging
DmcRodentTwoTouch-v1	38	30 s	Touch a target twice at the required interval
DmcCmuHumanoidTracking-v1	56	30 s	All 36 WALK_TINY motion capture clips
DmcSoccerBoxhead-v1	3 per player	45 s	Soccer with BoxHead walkers
DmcSoccerAnt-v1	8 per player	45 s	Soccer with Ant walkers
DmcSoccerHumanoid-v1	56 per player	45 s	Soccer with CMU humanoid walkers

All actions are float64 with bounds [-1, 1]. Control time steps are 0.03 s for CMU examples, 0.02 s for rodent examples, and 0.025 s for Soccer. time_limit overrides the task’s limit in seconds; max_episode_steps can impose a shorter limit in control steps. Failures and task completion can end an episode earlier. Tracking clip boundaries preserve the upstream discount of one and set the Gymnasium truncated flag.

Observations and rendering

Gymnasium observations are dictionaries with the original upstream keys, including slashes such as walker/joints_pos. The dm_env API exposes the same dictionary as timestep.observation.obs alongside EnvPool’s environment and player identifiers. Shapes and data types match the upstream observation specification, with the leading EnvPool batch dimension added.

The eight corridor, maze, bowl, and two-touch examples include the upstream 64 x 64 x 3 egocentric camera observation. Go-to-target, tracking, and Soccer expose their upstream state observations. Every task also supports native render_mode="rgb_array" and batched render(env_ids=[...]). Images are uint8 in height-width-channel order.

import envpool
import numpy as np

env = envpool.make_gymnasium(
    "DmcRodentEscapeBowl-v1",
    num_envs=4,
    seed=0,
    render_mode="rgb_array",
    render_width=320,
    render_height=240,
)
observation, info = env.reset()
observation, reward, terminated, truncated, info = env.step(
    np.zeros((4, 38), dtype=np.float64)
)
frames = env.render(env_ids=[3, 1])  # (2, 240, 320, 3)

The following comparisons cover every task after four external action steps, using one physics-state synchronization immediately after reset. EnvPool is on the left and the pinned official environment is on the right. The generator checks every displayed image for pixel equality before writing the figures.

Regenerate the figures with the test-only oracle and the shared MuJoCo engine:

bazel run --config=test //envpool/mujoco/locomotion:render_doc -- \
    --output "$PWD/docs/_static/render_samples"

Soccer players

team_size defaults to two and supports one through eleven players per team. max_num_players is derived as 2 * team_size. Player ordering within each match is all home players followed by all away players. Actions, observations, rewards, and discounts use the player batch dimension; step_type, terminated, and truncated use the match batch dimension. Use info["players"]["env_id"] (or timestep.observation.players.env_id) to associate players with their matches.

Soccer retains the upstream singleton observation buffer dimension: for example, BoxHead joint positions have shape (players, 1, 1). Rewards are float32 per player, as in the official Soccer API. Single-player Composer rewards retain float64 precision.

soccer = envpool.make_gymnasium(
    "DmcSoccerBoxhead-v1", num_envs=8, team_size=2, seed=0
)
observation, info = soccer.reset()
action = np.zeros((32, 3), dtype=np.float64)
observation, reward, terminated, truncated, info = soccer.step(
    action, env_id=info["env_id"]
)
# reward.shape == (32,), terminated.shape == truncated.shape == (8,)

The disable_walker_contacts, enable_field_box, keep_aspect_ratio, and terminate_on_goal options follow soccer.load. With terminate_on_goal=False, a goal awards the per-team reward and restarts play inside the same episode. Out-of-bounds balls are returned to play on the next control step unless the field box is enabled.

Reproducibility and assets

Each environment has independent random streams, including the draws that the upstream maze and two-touch examples make through NumPy’s global random state. Resetting another environment cannot change its rollout. Seeds are configured when creating the pool, following the normal EnvPool seed API.

On macOS, Apple’s CGL/Metal renderer can return slightly different pixels for identical model arrays, camera, lights, geometry, and skin vertices and normal vectors. This also reproduces with the official renderer alone, with serialized calls and with dithering, multisampling, or shadows disabled. Visual settings remain unchanged. The four CMU egocentric cameras allow at most five color levels per channel and a total absolute error of 20 across the entire 64-by-64 frame; the rodent maze camera allows one level in one channel. Public renders allow one level in at most three channels for go-to-target and escape-bowl, or one channel for tracking and heterogeneous forage. Other images and all native dynamics/reward replays remain bitwise. These limits are checked per frame, not averaged over a rollout.

Oracle reward checks retain only small derived-math residuals: tracking’s quaternion/exponential reductions, the bowl’s distance norm, and, on Linux x86-64 only, up to two ULPs in TwoTouch’s exponential shaping reward. The underlying MuJoCo state and discrete task transitions are checked exactly.

On every platform, the oracle builds both pinned MuJoCo and LabMaze’s official 1.0.6 Python binding from source with the same toolchain as EnvPool. LabMaze’s seeded layouts depend on the C++ standard library’s distribution and shuffle algorithms, so a published wheel can produce different mazes even on the same platform. This aligns the native and oracle builds locally; it does not guarantee identical mazes across platforms. No maze or task algorithm is patched, and model geometry is checked before reset-state sync.

Official XML, skins, textures, and LabMaze 1.0.6 sources are fetched at build time. The needed texture styles, model assets, motion clips, and license notices ship separately in envpool-assets>=0.4.1,<0.5.0, keeping the main envpool wheel below the PyPI size limit. Source builds use the generated Bazel assets directly, and the main wheel retains the native code’s license notices. The CMU 2019 clip used to initialize Soccer and all 36 CMU 2020 WALK_TINY clips are extracted from the official, SHA-256-pinned datasets into approximately 7 MB of native data. Runtime installation does not download the full motion capture datasets or require HDF5, SciPy, LabMaze’s Python extension, or dm_control.

The source models and tasks are available in the official locomotion package. Original motion capture data is provided by Carnegie Mellon University and fitted to the humanoid by the dm_control authors. License notices are included with the packaged assets.