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.
+Fancy Gym provides access to a range of environments for the Robot Air Hockey Challenge. 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.
diff --git a/docs/build/html/envs/fancy/mujoco.html b/docs/build/html/envs/fancy/mujoco.html index 0d07dfb..2164308 100644 --- a/docs/build/html/envs/fancy/mujoco.html +++ b/docs/build/html/envs/fancy/mujoco.html @@ -172,7 +172,7 @@
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 table tennis task offers a robotic arm equipped with seven degrees of freedom (DoFs). The task 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.
@@ -405,7 +405,7 @@ No longer used?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>.
Most of these envs also exist as MP-variants. Refer to them using fancy_DMP/<name> fancy_ProMP/<name> or fancy_ProDMP/<name>.
Citing the Project
-To cite fancy gym in publications:
+To cite fancy_gym in publications:
@software{fancy_gym,
title = {Fancy Gym},
author = {Otto, Fabian and Celik, Onur and Roth, Dominik and Zhou, Hongyi},
diff --git a/docs/build/html/searchindex.js b/docs/build/html/searchindex.js
index 6698ac4..9daa54b 100644
--- a/docs/build/html/searchindex.js
+++ b/docs/build/html/searchindex.js
@@ -1 +1 @@
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\ No newline at end of file
+Search.setIndex({"docnames": ["api", "envs/dmc", "envs/fancy/airhockey", "envs/fancy/classic_control", "envs/fancy/index", "envs/fancy/mujoco", "envs/meta", "envs/open_ai", "examples/dmc", "examples/general", "examples/metaworld", "examples/movement_primitives", "examples/mp_params_tuning", "examples/open_ai", "examples/pd_control_gain_tuning", "examples/replanning_envs", "generated/fancy_gym.envs", "generated/fancy_gym.register", "generated/fancy_gym.upgrade", "guide/basic_usage", "guide/episodic_rl", "guide/installation", "guide/upgrading_envs", "index"], "filenames": ["api.rst", "envs/dmc.md", "envs/fancy/airhockey.rst", "envs/fancy/classic_control.md", "envs/fancy/index.rst", "envs/fancy/mujoco.md", "envs/meta.md", "envs/open_ai.md", "examples/dmc.rst", "examples/general.rst", "examples/metaworld.rst", "examples/movement_primitives.rst", "examples/mp_params_tuning.rst", "examples/open_ai.rst", "examples/pd_control_gain_tuning.rst", "examples/replanning_envs.rst", "generated/fancy_gym.envs.rst", "generated/fancy_gym.register.rst", "generated/fancy_gym.upgrade.rst", "guide/basic_usage.rst", "guide/episodic_rl.rst", "guide/installation.rst", "guide/upgrading_envs.rst", "index.rst"], "titles": ["API", "DeepMind Control (DMC)", "AirHockey", "Classic Control", "Fancy", "Mujoco", "Metaworld", "Gymnasium", "DeepMind Control Examples", "General Usage Examples", "Metaworld Examples", "Movement Primitives Examples", "MP Params Tuning Example", "OpenAI Envs Examples", "PD Control Gain Tuning Example", "Replanning Example", "fancy_gym.envs", "fancy_gym.register", "fancy_gym.upgrade", "Basic Usage", "What is Episodic RL?", "Installation", "Creating new MP Environments", "Fancy Gym"], "terms": {"These": [1, 2, 3, 5, 7, 20], "ar": [1, 2, 3, 4, 5, 7, 8, 10, 11, 14, 17, 19, 20, 21, 22], "wrapper": [1, 8, 10, 11, 15, 17, 18, 22], "select": [1, 7, 22], "order": 1, "us": [1, 2, 5, 6, 9, 11, 15, 17, 18, 19, 20, 21, 22, 23], "our": [1, 8, 9, 10, 11, 20, 23], 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\ No newline at end of file
diff --git a/docs/source/envs/fancy/airhockey.rst b/docs/source/envs/fancy/airhockey.rst
index 9a89ca9..7b9b46b 100644
--- a/docs/source/envs/fancy/airhockey.rst
+++ b/docs/source/envs/fancy/airhockey.rst
@@ -1,7 +1,7 @@
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.
+Fancy Gym provides access to a range of environments for the `Robot Air Hockey Challenge
-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 table tennis task offers a robotic arm equipped with seven degrees of freedom (DoFs). The task 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.
@@ -118,4 +118,4 @@ No longer used?
## MP Environments
-Many of these envs also exist as MP-variants. Refer to them using `fancy_DMP/` `fancy_ProMP/` or `fancy_ProDMP/`.
+Most of these envs also exist as MP-variants. Refer to them using `fancy_DMP/` `fancy_ProMP/` or `fancy_ProDMP/`.