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docs/build/html/_sources/envs/dmc.md.txt
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# DeepMind Control (DMC)
<!-- TODO: Add Vid -->
These are the Environment Wrappers for selected
[DeepMind Control](https://github.com/google-deepmind/dm_control)
environments in order to use our Motion Primitive gym interface with them.
## Step-Based Environments
When installing fancy_gym with the optional dmc extra (e.g. `pip install fancy_gym[dmc]`), all regular dmc tasks are avaible via [Shimmy](https://github.com/Farama-Foundation/Shimmy).
| Name | Description | Action Dim | Observation Dim |
| --------------------------------------- | ----------------------------------------------------------------------------------------------------------------------- | ---------- | --------------- |
| `dm_control/acrobot-swingup-v0` | Underactuated double pendulum (Acrobot) with torque applied to the second joint to swing up and balance. | 1 | 6 |
| `dm_control/acrobot-swingup_sparse-v0` | Similar to `acrobot-swingup-v0`, but with sparse rewards for achieving the swingup task. | 1 | 6 |
| `dm_control/ball_in_cup-catch-v0` | Planar ball-in-cup task where a receptacle must swing to catch a ball. Sparse reward for catching. | 2 | 8 |
| `dm_control/cartpole-balance-v0` | Cart-pole task where the goal is to balance an unactuated pole by moving a cart, starting near upright. | 1 | 5 |
| `dm_control/cartpole-balance_sparse-v0` | Similar to `cartpole-balance-v0`, but with sparse rewards. | 1 | 5 |
| `dm_control/cartpole-swingup-v0` | Cart-pole task with the pole starting downward, requiring swinging up and balancing. | 1 | 5 |
| `dm_control/cartpole-swingup_sparse-v0` | Similar to `cartpole-swingup-v0`, but with sparse rewards for the swingup task. | 1 | 5 |
| `dm_control/cartpole-two_poles-v0` | Extension of the Cart-pole domain with two serially connected poles, increasing the balancing challenge. | 1 | 8 |
| `dm_control/cartpole-three_poles-v0` | Extension of the Cart-pole domain with three serially connected poles, further increasing the challenge. | 1 | 11 |
| `dm_control/cheetah-run-v0` | Planar bipedal cheetah robot tasked with running. The reward is proportional to forward velocity up to a maximum speed. | 6 | 17 |
| `dm_control/dog-stand-v0` | Dog robot task focusing on achieving a standing posture. | 38 | 223 |
| `dm_control/dog-walk-v0` | Dog robot tasked with walking, requiring coordination of movements. | 38 | 223 |
| `dm_control/dog-trot-v0` | Dog robot performing a trotting gait. | 38 | 223 |
| `dm_control/dog-run-v0` | Dog robot tasked with running, combining speed with stability. | 38 | 223 |
| `dm_control/dog-fetch-v0` | Dog robot playing fetch, involving locomotion and object interaction. | 38 | 232 |
| `dm_control/finger-spin-v0` | Finger robot required to rotate an unactuated body on a hinge. | 2 | 9 |
| `dm_control/finger-turn_easy-v0` | Finger robot task to align the tip of a free body with a target, easier version with a larger target. | 2 | 12 |
| `dm_control/finger-turn_hard-v0` | Similar to `finger-turn_easy-v0`, but with a smaller target for increased difficulty. | 2 | 12 |
| `dm_control/fish-upright-v0` | Fish robot task focused on righting itself in a fluid environment. | 5 | 21 |
| `dm_control/fish-swim-v0` | Fish robot swimming to a target, incorporating fluid dynamics. | 5 | 24 |
| `dm_control/hopper-stand-v0` | One-legged hopper robot tasked with achieving a minimal torso height. | 4 | 15 |
| `dm_control/hopper-hop-v0` | One-legged hopper robot required to hop, combining height and forward velocity. | 4 | 15 |
| `dm_control/humanoid-stand-v0` | Simplified humanoid robot maintaining a standing posture. | 21 | 67 |
| `dm_control/humanoid-walk-v0` | Simplified humanoid robot walking at a specified speed. | 21 | 67 |
| `dm_control/humanoid-run-v0` | Simplified humanoid robot running, aiming for a high horizontal speed. | 21 | 67 |
| `dm_control/humanoid-run_pure_state-v0` | Simplified humanoid robot running with a focus on pure state control. | 21 | 55 |
| `dm_control/humanoid_CMU-stand-v0` | Advanced humanoid robot (CMU model) maintaining a standing posture. | 56 | 137 |
| `dm_control/humanoid_CMU-walk-v0` | Advanced humanoid robot (CMU model) walking. | 56 | 137 |
| `dm_control/humanoid_CMU-run-v0` | Advanced humanoid robot (CMU model) running. | 56 | 137 |
| `dm_control/lqr-lqr_2_1-v0` | Linear quadratic regulator (LQR) task with 2 masses and 1 actuator, focusing on position and control optimization. | 1 | 4 |
| `dm_control/lqr-lqr_6_2-v0` | Linear quadratic regulator (LQR) task with 6 masses and 2 actuators, more complex control optimization challenge. | 2 | 12 |
| `dm_control/manipulator-bring_ball-v0` | Planar manipulator robot bringing a ball to a target location, with object initialization variations. | 5 | 44 |
| `dm_control/manipulator-bring_peg-v0` | Planar manipulator task of bringing a peg to a target peg. | 5 | 44 |
| `dm_control/manipulator-insert_ball-v0` | Planar manipulator task requiring inserting a ball into a basket. | 5 | 44 |
| `dm_control/manipulator-insert_peg-v0` | Planar manipulator challenge of inserting a peg into a slot. | 5 | 44 |
| `dm_control/pendulum-swingup-v0` | Classic inverted pendulum task with a torque-limited actuator, requiring multiple swings to swing up and balance. | 1 | 3 |
| `dm_control/point_mass-easy-v0` | Planar point mass in an easy task, with actuators corresponding to global x and y axes | 2 | 4 |
| `dm_control/point_mass-hard-v0` | Planar point mass in a hard task, with randomized control gains per episode, challenging memoryless agents. | 2 | 4 |
| `dm_control/quadruped-walk-v0` | Four-legged robot (Quadruped) tasked with walking. | 12 | 78 |
| `dm_control/quadruped-run-v0` | Quadruped robot required to run, balancing speed and stability. | 12 | 78 |
| `dm_control/quadruped-escape-v0` | Quadruped robot in an escape task, combining locomotion with environmental interaction. | 12 | 101 |
| `dm_control/quadruped-fetch-v0` | Quadruped robot playing fetch, involving complex movements and object interaction. | 12 | 90 |
| `dm_control/reacher-easy-v0` | Two-link planar reacher with a randomized target, easier version with a larger target sphere. | 2 | 6 |
| `dm_control/reacher-hard-v0` | Similar to `reacher-easy-v0`, but with a smaller target sphere for increased difficulty. | 2 | 6 |
| `dm_control/stacker-stack_2-v0` | Stacker task requiring stacking of 2 boxes, with rewards for correct placement and gripper positioning. | 5 | 49 |
| `dm_control/stacker-stack_4-v0` | More complex stacker task with 4 boxes, increasing the stacking challenge. | 5 | 63 |
| `dm_control/swimmer-swimmer6-v0` | Six-link planar swimmer in fluid dynamics, rewarded for positioning the nose inside a target. | 5 | 25 |
| `dm_control/swimmer-swimmer15-v0` | Fifteen-link planar swimmer, a more complex version of the swimmer task with extended dynamics. | 14 | 61 |
| `dm_control/walker-stand-v0` | Planar walker robot tasked with maintaining an upright torso and minimal height. | 6 | 24 |
| `dm_control/walker-walk-v0` | Planar walker robot walking task, focusing on forward velocity and stability. | 6 | 24 |
| `dm_control/walker-run-v0` | Planar walker robot running task, aiming for high speed and balance. | 6 | 24 |
## MP Environments
[//]: <> (These environments are wrapped-versions of their Deep Mind Control Suite &#40;DMC&#41; counterparts. Given most task can be)
[//]: <> (solved in shorter horizon lengths than the original 1000 steps, we often shorten the episodes for those task.)
| Name | Description | Trajectory Horizon | Action Dimension | Context Dimension |
| ---------------------------------------- | ------------------------------------------------------------------------------ | ------------------ | ---------------- | ----------------- |
| `dm_control_ProDMP/ball_in_cup-catch-v0` | A ProMP wrapped version of the "catch" task for the "ball_in_cup" environment. | 1000 | 10 | 2 |
| `dm_control_DMP/ball_in_cup-catch-v0` | A DMP wrapped version of the "catch" task for the "ball_in_cup" environment. | 1000 | 10 | 2 |
| `dm_control_ProDMP/reacher-easy-v0` | A ProMP wrapped version of the "easy" task for the "reacher" environment. | 1000 | 10 | 4 |
| `dm_control_DMP/reacher-easy-v0` | A DMP wrapped version of the "easy" task for the "reacher" environment. | 1000 | 10 | 4 |
| `dm_control_ProDMP/reacher-hard-v0` | A ProMP wrapped version of the "hard" task for the "reacher" environment. | 1000 | 10 | 4 |
| `dm_control_DMP/reacher-hard-v0` | A DMP wrapped version of the "hard" task for the "reacher" environment. | 1000 | 10 | 4 |
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AirHockey
=========
Fancy Gym provides access to a range of environments for the Robot Air Hockey Challenge, facilitating research in robot learning. The challenge aims to close the gap between simulated learning and real-world application by focusing on various aspects of robotic operation, such as dealing with disturbances, observation noise, safety, and actuator limitations.
The environments available through Fancy Gym allow for the development of agents capable of performing tasks with different levels of complexity. These tasks include hitting and defending in air hockey with both three degrees of freedom (3 DoF) and seven degrees of freedom (7 DoF) configurations. The 7 DoF tasks are based on the KUKA iiwa14 robot model, which is used in the simulations to represent a higher level of control complexity akin to real-world settings.
Participants in the challenge are required to develop strategies that enable their robots to react and adapt within these dynamic environments. The final phase of the challenge involves a tournament where the developed agents will be tested in a comprehensive game scenario, both in simulation and, for the top teams, on actual robotic systems.
For detailed information on the challenge, including rules, stages, and submission requirements, please visit the `official Robot Air Hockey Challenge website <https://air-hockey-challenge.robot-learning.net/>`_.
The available environments are as follows:
- fancy/AirHockey-7dof-hit-v0
- fancy/AirHockey-7dof-defend-v0
- fancy/AirHockey-3dof-hit-v0
- fancy/AirHockey-3dof-defend-v0
- fancy/AirHockey-7dof-hit-airhockit2023-v0
- fancy/AirHockey-7dof-defend-airhockit2023-v0
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# Classic Control
Classic Control environments provide a foundational platform for exploring and experimenting with RL algorithms. These environments are designed to be simple, allowing researchers and practitioners to focus on the fundamental principles of control without the complexities of high-dimensional and physics-based simulations.
## Step-based Environments
| Name | Description | Horizon | Action Dimension | Observation Dimension |
| ---------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------- | ---------------- | --------------------- |
| `fancy/SimpleReacher-v0` | Simple reaching task (2 links) without any physics simulation. Provides no reward until 150 time steps. This allows the agent to explore the space, but requires precise actions towards the end of the trajectory. | 200 | 2 | 9 |
| `fancy/LongSimpleReacher-v0` | Simple reaching task (5 links) without any physics simulation. Provides no reward until 150 time steps. This allows the agent to explore the space, but requires precise actions towards the end of the trajectory. | 200 | 5 | 18 |
| `fancy/ViaPointReacher-v0` | Simple reaching task leveraging a via point, which supports self collision detection. Provides a reward only at 100 and 199 for reaching the viapoint and goal point, respectively. | 200 | 5 | 18 |
| `fancy/HoleReacher-v0` | 5 link reaching task where the end-effector needs to reach into a narrow hole without collding with itself or walls. | 200 | 5 | 18 |
## MP Environments
| Name | Description | Horizon | Action Dimension | Context Dimension |
| ----------------------------------- | -------------------------------------------------------------------------------------------------------- | ------- | ---------------- | ----------------- |
| `fancy_DMP/ViaPointReacher-v0` | A DMP provides a trajectory for the `fancy/ViaPointReacher-v0` task. | 200 | 25 |
| `fancy_DMP/HoleReacherFixedGoal-v0` | A DMP provides a trajectory for the `fancy/HoleReacher-v0` task with a fixed goal attractor. | 200 | 25 |
| `fancy_DMP/HoleReacher-v0` | A DMP provides a trajectory for the `fancy/HoleReacher-v0` task. The goal attractor needs to be learned. | 200 | 30 |
[//]: |`fancy/HoleReacherProMPP-v0`|
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Fancy
=====
Fancy Gym adds a couple new environments. Some of these are adaptations of existing environments while others were designed and build by us from the ground up.
.. raw:: html
<div class='center'>
<img src="../../_static/imgs/env_gifs/Box_Pushing.gif" style="margin: 5%; width: 45%;">
<p>Box Pushing Task (<a href="./mujoco.html#box-pushing">fancy/BoxPushingDense-v0</a>)</p>
</div>
<br>
.. toctree::
mujoco.md
airhockey
classic_control.md
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# Mujoco
## Step-based Environments
### Box Pushing
<div class='center'>
<img src="../../_static/imgs/env_gifs/Box_Pushing.gif" style="margin: 5%; width: 45%;">
</div>
The box-pushing task presents an advanced environment for reinforcement learning (RL) systems, utilizing the versatile Franka Emika Panda robotic arm, which boasts seven degrees of freedom (DoFs). The objective of this task is to precisely manipulate a box to a specified goal location and orientation.
This environment defines its context space with a goal position constrained within a certain range along the x and y axes and a goal orientation that encompasses the full 360-degree range on the z-axis. The robot's mission is to achieve positional accuracy within 5 centimeters and an orientation accuracy within 0.5 radians of the specified goal.
The observation space includes the sine and cosine values of the robotic joint angles, their velocities, and quaternion orientations for the end-effector and the box. The action space describes the applied torques for each joint.
A composite reward function serves as the performance metric for the RL system. It accounts for the distance to the goal, the box's orientation, maintaining a rod within the box, achieving the rod's desired orientation, and includes penalties for joint position and velocity limit violations, as well as an action cost for energy expenditure.
Variations of this environment are available, differing in reward structures and the optionality of randomizing the box's initial position. These variations are purposefully designed to challenge RL algorithms, enhancing their generalization and adaptation capabilities. Temporally sparse environments only provide a reward at the last timestep. Spatially sparse environments only provide a reward, if the goal is almost reached, the box is close enought to the goal and somewhat correctly aligned.
| Name | Description | Horizon | Action Dimension | Observation Dimension |
| ------------------------------------------ | -------------------------------------------------------------------- | ------- | ---------------- | --------------------- |
| `fancy/BoxPushingDense-v0` | Custom Box-pushing task with dense rewards | 100 | 3 | 13 |
| `fancy/BoxPushingTemporalSparse-v0` | Custom Box-pushing task with temporally sparse rewards | 100 | 3 | 13 |
| `fancy/BoxPushingTemporalSpatialSparse-v0` | Custom Box-pushing task with temporally and spatially sparse rewards | 100 | 3 | 13 |
---
### Table Tennis
<div class='center'>
<img src="../../_static/imgs/env_gifs/Table_Tennis.gif" style="margin: 5%; width: 45%;">
</div>
The table tennis task offers a dynamic and interactive environment designed for the development and testing of reinforcement learning (RL) systems. Using a robotic arm equipped with seven degrees of freedom (DoFs), the challenge is to respond to incoming balls and return them accurately to a specified goal location on the opponent's side of the table.
The context space for this environment includes the initial ball position, with x-coordinates ranging from -1 to -0.2 meters and y-coordinates from -0.65 to 0.65 meters, and the goal position with x-coordinates between -1.2 to -0.2 meters and y-coordinates from -0.6 to 0.6 meters. The full observation space comprises the sine and cosine values of the joint angles, the joint velocities, and the ball's velocity, providing comprehensive information for the RL system to base its decisions on.
A task is considered successfully completed when the returned ball not only lands on the opponent's side of the table but also within a tight margin of 20 centimeters from the goal location. The reward function is designed to reflect various conditions of play, including whether the ball was hit, if it landed on the table, and the proximity of the ball's landing position to the goal location.
Variations of the table tennis environment are available to cater to different research needs. These variations maintain the foundational challenge of precise ball return while providing additional complexity for RL algorithms to overcome.
| Name | Description | Horizon | Action Dimension | Observation Dimension |
| ----------------------------------- | -------------------------------------------------------------------------------------------------- | ------- | ---------------- | --------------------- |
| `fancy/TableTennis2D-v0` | Table Tennis task with 2D context, based on a custom environment for table tennis | 350 | 7 | 19 |
| `fancy/TableTennis2DReplan-v0` | Table Tennis task with 2D context and replanning, based on a custom environment for table tennis | 350 | 7 | 19 |
| `fancy/TableTennis4D-v0` | Table Tennis task with 4D context, based on a custom environment for table tennis | 350 | 7 | 22 |
| `fancy/TableTennis4DReplan-v0` | Table Tennis task with 4D context and replanning, based on a custom environment for table tennis | 350 | 7 | 22 |
| `fancy/TableTennisWind-v0` | Table Tennis task with wind effects, based on a custom environment for table tennis | 350 | 7 | 19 |
| `fancy/TableTennisGoalSwitching-v0` | Table Tennis task with goal switching, based on a custom environment for table tennis | 350 | 7 | 19 |
| `fancy/TableTennisWindReplan-v0` | Table Tennis task with wind effects and replanning, based on a custom environment for table tennis | 350 | 7 | 19 |
---
### Beer Pong
<div class='center'>
<img src="../../_static/imgs/env_gifs/Beer_Pong.gif" style="margin: 5%; width: 45%;">
</div>
<!-- TODO: Vid is ugly and unsuccessful. Replace. -->
The Beer Pong task is based upon a robotic system with seven Degrees of Freedom (DoF), challenging the robot to throw a ball into a cup placed on a large table. The environment's context is established by the cup's location, defined within a range of x-coordinates from -1.42 to 1.42 meters and y-coordinates from -4.05 to -1.25 meters.
The observation space includes the cosine and sine of the robot's joint angles, the angular velocities, and distances of the ball relative to the top and bottom of the cup, along with the cup's position and the current timestep. The action space for the robot is defined by the torques applied to each joint. For episode-based methods, the parameter space is expanded to 15 dimensions, which includes two weights for the basis functions per joint and the duration of the throw, namely the ball release time.
Action penalties are implemented in the form of squared torque sums applied across all joints, penalizing excessive force and encouraging efficient motion. The reward function at each timestep t before the final timestep T penalizes the action penalty, while at t=T, a non-Markovian reward based on the ball's position relative to the cup and the action penalty is considered.
Conditions for the task are specified as follows:
- The ball contacts the ground before touching the table.
- The ball is not in the cup and has not made contact with the table.
- The ball is not in the cup but has made contact with the table.
- The ball successfully lands in the cup.
An additional reward component at the final timestep T assesses the chosen ball release time to ensure it falls within a reasonable range. The overall return for an episode is the sum of the rewards at each timestep, the task-specific reward, and the release time reward.
A successful throw in this task is determined by the ball landing in the cup at the episode's conclusion, showcasing the robot's ability to accurately predict and execute the complex motion required for this popular party game.
| Name | Description | Horizon | Action Dimension | Observation Dimension |
| ------------------------------- | ---------------------------------------------------------------------------------------------- | ------- | ---------------- | --------------------- |
| `fancy/BeerPong-v0` | Beer Pong task, based on a custom environment with multiple task variations | 300 | 3 | 29 |
| `fancy/BeerPongStepBased-v0` | Step-based rewards for the Beer Pong task, based on a custom environment with episodic rewards | 300 | 3 | 29 |
| `fancy/BeerPongFixedRelease-v0` | Beer Pong with fixed release, based on a custom environment with episodic rewards | 300 | 3 | 29 |
---
### Variations of existing environments
| Name | Description | Horizon | Action Dimension | Observation Dimension |
| ------------------------------ | ------------------------------------------------------------------------------------------------ | ------- | ---------------- | --------------------- |
| `fancy/Reacher-v0` | Modified (5 links) gymnasiums's mujoco `Reacher-v2` (2 links) | 200 | 5 | 21 |
| `fancy/ReacherSparse-v0` | Same as `fancy/Reacher-v0`, but the distance penalty is only provided in the last time step. | 200 | 5 | 21 |
| `fancy/LongReacher-v0` | Modified (7 links) gymnasiums's mujoco `Reacher-v2` (2 links) | 200 | 7 | 27 |
| `fancy/LongReacherSparse-v0` | Same as `fancy/LongReacher-v0`, but the distance penalty is only provided in the last time step. | 200 | 7 | 27 |
| `fancy/Reacher5d-v0` | Reacher task with 5 links, based on Gymnasium's `gym.envs.mujoco.ReacherEnv` | 200 | 5 | 20 |
| `fancy/Reacher5dSparse-v0` | Sparse Reacher task with 5 links, based on Gymnasium's `gym.envs.mujoco.ReacherEnv` | 200 | 5 | 20 |
| `fancy/Reacher7d-v0` | Reacher task with 7 links, based on Gymnasium's `gym.envs.mujoco.ReacherEnv` | 200 | 7 | 22 |
| `fancy/Reacher7dSparse-v0` | Sparse Reacher task with 7 links, based on Gymnasium's `gym.envs.mujoco.ReacherEnv` | 200 | 7 | 22 |
| `fancy/HopperJumpSparse-v0` | Hopper Jump task with sparse rewards, based on Gymnasium's `gym.envs.mujoco.Hopper` | 250 | 3 | 15 / 16\* |
| `fancy/HopperJump-v0` | Hopper Jump task with continuous rewards, based on Gymnasium's `gym.envs.mujoco.Hopper` | 250 | 3 | 15 / 16\* |
| `fancy/AntJump-v0` | Ant Jump task, based on Gymnasium's `gym.envs.mujoco.Ant` | 200 | 8 | 119 |
| `fancy/HalfCheetahJump-v0` | HalfCheetah Jump task, based on Gymnasium's `gym.envs.mujoco.HalfCheetah` | 100 | 6 | 112 |
| `fancy/HopperJumpOnBox-v0` | Hopper Jump on Box task, based on Gymnasium's `gym.envs.mujoco.Hopper` | 250 | 4 | 16 / 100\* |
| `fancy/HopperThrow-v0` | Hopper Throw task, based on Gymnasium's `gym.envs.mujoco.Hopper` | 250 | 3 | 18 / 100\* |
| `fancy/HopperThrowInBasket-v0` | Hopper Throw in Basket task, based on Gymnasium's `gym.envs.mujoco.Hopper` | 250 | 3 | 18 / 100\* |
| `fancy/Walker2DJump-v0` | Walker 2D Jump task, based on Gymnasium's `gym.envs.mujoco.Walker2d` | 300 | 6 | 18 / 19\* |
\*Observation dimensions depend on configuration.
<!--
No longer used?
| Name | Description | Horizon | Action Dimension | Observation Dimension |
| --------------------------- | --------------------------------------------------------------------------------------------------- | ------- | ---------------- | --------------------- |
| `fancy/BallInACupSimple-v0` | Ball-in-a-cup task where a robot needs to catch a ball attached to a cup at its end-effector. | 4000 | 3 | wip |
| `fancy/BallInACup-v0` | Ball-in-a-cup task where a robot needs to catch a ball attached to a cup at its end-effector | 4000 | 7 | wip |
| `fancy/BallInACupGoal-v0` | Similar to `fancy/BallInACupSimple-v0` but the ball needs to be caught at a specified goal position | 4000 | 7 | wip |
-->
## MP Environments
Many of these envs also exist as MP-variants. Refer to them using `fancy_DMP/<name>` `fancy_ProMP/<name>` or `fancy_ProDMP/<name>`.
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# Metaworld
<div class='center'>
<img src="../_static/imgs/env_gifs/Metaworld.gif" style="margin: 5%; width: 45%;">
<p>Metaworld Dial Turn Task (metaworld/dial-turn-v2)</p>
</div>
<br>
[Metaworld](https://meta-world.github.io/) is an open-source simulated benchmark designed to advance meta-reinforcement learning and multi-task learning, comprising 50 diverse robotic manipulation tasks. The benchmark features a universal tabletop environment equipped with a simulated Sawyer arm and a variety of everyday objects. This shared environment is pivotal for reusing structured learning and efficiently acquiring related tasks.
## Step-Based Environments
`fancy_gym` makes all metaworld ML1 tasks avaible via the standard gym interface.
| Name | Description | Horizon | Action Dimension | Observation Dimension | Context Dimension |
| ---------------------------------------- | ------------------------------------------------------------------------------------- | ------- | ---------------- | --------------------- | ----------------- |
| `metaworld/assembly-v2` | A task where the robot must assemble components. | 150 | 4 | 39 | 6 |
| `metaworld/basketball-v2` | A task where the robot must play a game of basketball. | 150 | 4 | 39 | 6 |
| `metaworld/bin-picking-v2` | A task involving the robot picking objects from a bin. | 150 | 4 | 39 | 6 |
| `metaworld/box-close-v2` | A task requiring the robot to close a box. | 150 | 4 | 39 | 6 |
| `metaworld/button-press-topdown-v2` | A task where the robot must press a button from a top-down perspective. | 150 | 4 | 39 | 6 |
| `metaworld/button-press-topdown-wall-v2` | A task involving the robot pressing a button with a wall from a top-down perspective. | 150 | 4 | 39 | 6 |
| `metaworld/button-press-v2` | A task where the robot must press a button. | 150 | 4 | 39 | 6 |
| `metaworld/button-press-wall-v2` | A task involving the robot pressing a button with a wall. | 150 | 4 | 39 | 6 |
| `metaworld/coffee-button-v2` | A task where the robot must press a button on a coffee machine. | 150 | 4 | 39 | 6 |
| `metaworld/coffee-pull-v2` | A task involving the robot pulling a lever on a coffee machine. | 150 | 4 | 39 | 6 |
| `metaworld/coffee-push-v2` | A task involving the robot pushing a component on a coffee machine. | 150 | 4 | 39 | 6 |
| `metaworld/dial-turn-v2` | A task where the robot must turn a dial. | 150 | 4 | 39 | 6 |
| `metaworld/disassemble-v2` | A task requiring the robot to disassemble an object. | 150 | 4 | 39 | 6 |
| `metaworld/door-close-v2` | A task where the robot must close a door. | 150 | 4 | 39 | 6 |
| `metaworld/door-lock-v2` | A task involving the robot locking a door. | 150 | 4 | 39 | 6 |
| `metaworld/door-open-v2` | A task where the robot must open a door. | 150 | 4 | 39 | 6 |
| `metaworld/door-unlock-v2` | A task involving the robot unlocking a door. | 150 | 4 | 39 | 6 |
| `metaworld/hand-insert-v2` | A task requiring the robot to insert a hand into an object. | 150 | 4 | 39 | 6 |
| `metaworld/drawer-close-v2` | A task where the robot must close a drawer. | 150 | 4 | 39 | 6 |
| `metaworld/drawer-open-v2` | A task involving the robot opening a drawer. | 150 | 4 | 39 | 6 |
| `metaworld/faucet-open-v2` | A task requiring the robot to open a faucet. | 150 | 4 | 39 | 6 |
| `metaworld/faucet-close-v2` | A task where the robot must close a faucet. | 150 | 4 | 39 | 6 |
| `metaworld/hammer-v2` | A task where the robot must use a hammer. | 150 | 4 | 39 | 6 |
| `metaworld/handle-press-side-v2` | A task involving the robot pressing a handle from the side. | 150 | 4 | 39 | 6 |
| `metaworld/handle-press-v2` | A task where the robot must press a handle. | 150 | 4 | 39 | 6 |
| `metaworld/handle-pull-side-v2` | A task requiring the robot to pull a handle from the side. | 150 | 4 | 39 | 6 |
| `metaworld/handle-pull-v2` | A task where the robot must pull a handle. | 150 | 4 | 39 | 6 |
| `metaworld/lever-pull-v2` | A task involving the robot pulling a lever. | 150 | 4 | 39 | 6 |
| `metaworld/peg-insert-side-v2` | A task requiring the robot to insert a peg from the side. | 150 | 4 | 39 | 6 |
| `metaworld/pick-place-wall-v2` | A task involving the robot picking and placing an object with a wall. | 150 | 4 | 39 | 6 |
| `metaworld/pick-out-of-hole-v2` | A task where the robot must pick an object out of a hole. | 150 | 4 | 39 | 6 |
| `metaworld/reach-v2` | A task where the robot must reach an object. | 150 | 4 | 39 | 6 |
| `metaworld/push-back-v2` | A task involving the robot pushing an object backward. | 150 | 4 | 39 | 6 |
| `metaworld/push-v2` | A task where the robot must push an object. | 150 | 4 | 39 | 6 |
| `metaworld/pick-place-v2` | A task involving the robot picking up and placing an object. | 150 | 4 | 39 | 6 |
| `metaworld/plate-slide-v2` | A task requiring the robot to slide a plate. | 150 | 4 | 39 | 6 |
| `metaworld/plate-slide-side-v2` | A task involving the robot sliding a plate from the side. | 150 | 4 | 39 | 6 |
| `metaworld/plate-slide-back-v2` | A task where the robot must slide a plate backward. | 150 | 4 | 39 | 6 |
| `metaworld/plate-slide-back-side-v2` | A task involving the robot sliding a plate backward from the side. | 150 | 4 | 39 | 6 |
| `metaworld/peg-unplug-side-v2` | A task where the robot must unplug a peg from the side. | 150 | 4 | 39 | 6 |
| `metaworld/soccer-v2` | A task where the robot must play soccer. | 150 | 4 | 39 | 6 |
| `metaworld/stick-push-v2` | A task involving the robot pushing a stick. | 150 | 4 | 39 | 6 |
| `metaworld/stick-pull-v2` | A task where the robot must pull a stick. | 150 | 4 | 39 | 6 |
| `metaworld/push-wall-v2` | A task involving the robot pushing against a wall. | 150 | 4 | 39 | 6 |
| `metaworld/reach-wall-v2` | A task where the robot must reach an object with a wall. | 150 | 4 | 39 | 6 |
| `metaworld/shelf-place-v2` | A task involving the robot placing an object on a shelf. | 150 | 4 | 39 | 6 |
| `metaworld/sweep-into-v2` | A task where the robot must sweep objects into a container. | 150 | 4 | 39 | 6 |
| `metaworld/sweep-v2` | A task requiring the robot to sweep. | 150 | 4 | 39 | 6 |
| `metaworld/window-open-v2` | A task where the robot must open a window. | 150 | 4 | 39 | 6 |
| `metaworld/window-close-v2` | A task involving the robot closing a window. | 150 | 4 | 39 | 6 |
## MP Environments
All envs also exist in MP-variants. Refer to them using `metaworld_ProMP/<name-v2>` or `metaworld_ProDMP/<name-v2>` (DMP is currently not supported as of now).
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# Gymnasium
<div class='center'>
<img src="../_static/imgs/env_gifs/Lunar_Lander.gif" style="margin: 5%; width: 60%;">
<p>Lunar Lander Task (LunarLander-v2)</p>
</div>
<br>
We provide MP versions for selected [Farama Gymnasium](https://gymnasium.farama.org/) (previously OpenAI Gym) environments.
## Step-Based Environments
We refer to the [Gymnasium docs](https://gymnasium.farama.org/content/basic_usage/) for an overview of step-based environments provided by them.
## MP Environments
These environments are wrapped-versions of their Gymnasium counterparts.
| Name | Description | Trajectory Horizon | Action Dimension |
| ------------------------------------ | -------------------------------------------------------------------- | ------------------ | ---------------- |
| `gym_ProMP/ContinuousMountainCar-v0` | A ProMP wrapped version of the ContinuousMountainCar-v0 environment. | 100 | 1 |
| `gym_ProMP/Reacher-v2` | A ProMP wrapped version of the Reacher-v2 environment. | 50 | 2 |
| `gym_ProMP/FetchSlideDense-v1` | A ProMP wrapped version of the FetchSlideDense-v1 environment. | 50 | 4 |
| `gym_ProMP/FetchReachDense-v1` | A ProMP wrapped version of the FetchReachDense-v1 environment. | 50 | 4 |