Samples heights of the terrain at required points around each robot. The points are offset by the base's position and rotated by the base

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

Returns points at which the height measurments are sampled (in base frame) Returns: [torch.Tensor]: Tensor of shape (num_envs, s

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.

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

Creates simulation, terrain and evironments

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

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

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

default to uniform

Set camera position and direction