Final? touches to the docs

2024-01-23 17:42:39 +01:00
parent 6d583a36f4
commit 59c980e495
13 changed files with 12 additions and 12 deletions
@@ -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 <https://air-hockey-challenge.robot-learning.net/>`_. 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.

@@ -32,7 +32,7 @@ Variations of this environment are available, differing in reward structures and
   <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 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/<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>`.
@@ -134,7 +134,7 @@ Explore the MP-based variant (`or learn more about Movement Primitives (MPs) <gu
 Citing the Project
 ------------------

-To cite `fancy gym` in publications:
+To cite `fancy_gym` in publications:

 .. code:: bibtex