visualizers.viser_app.tasks.spot_base#

Module Contents#

class visualizers.viser_app.tasks.spot_base.GOAL_POSITIONS#

Goal positions of Spot. .. py:attribute:: origin

type:

numpy.ndarray

blue_cross: numpy.ndarray#
black_cross: numpy.ndarray#
visualizers.viser_app.tasks.spot_base.DEFAULT_SPOT_ROLLOUT_CUTOFF_TIME = 0.25#
class visualizers.viser_app.tasks.spot_base.SpotBaseConfig#

Bases: jacta.visualizers.viser_app.tasks.task.TaskConfig

Base config for spot tasks. .. py:attribute:: default_command

type:

numpy.ndarray

fall_penalty: float = 2500.0#
spot_fallen_threshold = 0.35#
w_goal: float = 60.0#
w_proximity: float = 2.0#
w_controls: float = 0.0#
cutoff_time: float#
visualizers.viser_app.tasks.spot_base.ConfigT#
class visualizers.viser_app.tasks.spot_base.SpotBase(model_filepath: str, policy_filepath: str)#

Bases: jacta.visualizers.viser_app.tasks.mujoco_task.MujocoTask[ConfigT]

Base task for spot locomotion.

This is an ‘abstract’ class that should not be instantiated. .. py:property:: dt

type:

float

Effective timestep for this task – each step is physics_substeps * the mujoco dt.

Parameters:

  • model_filepath (str) –

  • policy_filepath (str) –

property default_idle_command: numpy.ndarray#

Default idling command. Must be defined.

Return type:

numpy.ndarray

sim_step(controls: scipy.interpolate.interp1d | None) → None#

Spot policy physics evaluation. Evaluates policy and steps simulation forward.

Parameters:

controls (Optional[scipy.interpolate.interp1d]) – either a spline to be interpolated or None, which calls a default command

Return type:

None

rollout(models: list[mujoco_extensions.policy_rollout.System], states: numpy.ndarray, controls: numpy.ndarray, additional_info: dict[str, Any], output_states: numpy.ndarray, output_sensors: numpy.ndarray) → tuple[numpy.ndarray, numpy.ndarray]#

Threaded rollout for spot RL system.

Parameters:

  • models (list[mujoco_extensions.policy_rollout.System]) –

  • states (numpy.ndarray) –

  • controls (numpy.ndarray) –

  • additional_info (dict[str, Any]) –

  • output_states (numpy.ndarray) –

  • output_sensors (numpy.ndarray) –

Return type:

tuple[numpy.ndarray, numpy.ndarray]

make_models(num_models: int) → list[mujoco_extensions.policy_rollout.System]#

Allocates systems vector to be used for threaded rollout.

Parameters:

num_models (int) –

Return type:

list[mujoco_extensions.policy_rollout.System]