Sets a vector used to scale the noise added to the observations. [NOTE]: Must be adapted when changing the observations structure

Initialize torch tensors which will contain simulation states and processed quantities

Callback called before computing terminations, rewards, and observations Default behaviour: Compute ang vel command based on target and h

Prepares a list of reward functions, whcih will be called to compute the total reward. Looks for self._reward_<REWARD_NAME>, where <REWAR

Callback allowing to store/change/randomize the DOF properties of each environment. Called During environment creation. Base

Callback allowing to store/change/randomize the rigid shape properties of each environment. Called During environment creation.

Random pushes the robots. Emulates an impulse by setting a randomized base velocity.

Resets DOF position and velocities of selected environmments Positions are randomly selected within 0.5:1.5 x default positions. Velo

Resets ROOT states position and velocities of selected environmments Sets base position based on the curriculum Selects rando

Check if environments need to be reset

Computes observations

Compute rewards Calls each reward function which had a non-zero scale (processed in self._prepare_reward_function()) adds eac

Computes observations

Creates simulation, terrain and evironments

Get gravity vector in body frame

Get rotation matrix from the given quaternion. Args: q (np.array[float[4]]): quaternion [w,x,y,z] Returns: np.array[float

Load and process the YAML configuration file

check terminations, compute observations and rewards calls self._post_physics_step_callback() for common computations

Reset some environments. Calls self._reset_dofs(env_ids), self._reset_root_states(env_ids), and self._resample_commands(env_ids)

Implements a curriculum of increasing action range Args: env_ids (List[int]): ids of environments being reset

Implements a curriculum of increasing commands Args: env_ids (List[int]): ids of environments being reset