mani_skill.envs.tasks.rotate_cube#

Classes#

RotateCubeEnv

Modified from NVIDIA-Omniverse/IsaacGymEnvs

RotateCubeEnvLevel0

Modified from NVIDIA-Omniverse/IsaacGymEnvs

RotateCubeEnvLevel1

Modified from NVIDIA-Omniverse/IsaacGymEnvs

RotateCubeEnvLevel2

Modified from NVIDIA-Omniverse/IsaacGymEnvs

RotateCubeEnvLevel3

Modified from NVIDIA-Omniverse/IsaacGymEnvs

RotateCubeEnvLevel4

Modified from NVIDIA-Omniverse/IsaacGymEnvs

Module Contents#

class mani_skill.envs.tasks.rotate_cube.RotateCubeEnv(*args, robot_uids='trifingerpro', robot_init_qpos_noise=0.02, difficulty_level=4, **kwargs)[source]#

Bases: mani_skill.envs.sapien_env.BaseEnv

Modified from NVIDIA-Omniverse/IsaacGymEnvs NVIDIA-Omniverse/IsaacGymEnvs

Parameters:

difficulty_level (int) –

_get_obs_extra(info)[source]#

Get task-relevant extra observations. Usually defined on a task by task basis

Parameters:

info (dict) –

_initialize_actors(env_idx)[source]#
Parameters:

env_idx (torch.Tensor) –

_initialize_agent(env_idx)[source]#
Parameters:

env_idx (torch.Tensor) –

_initialize_episode(env_idx, options)[source]#

Initialize the episode, e.g., poses of actors and articulations, as well as task relevant data like randomizing goal positions

Parameters:

  • env_idx (torch.Tensor) –

  • options (dict) –

_load_scene(options)[source]#

Loads all objects like actors and articulations into the scene. Called by self._reconfigure. Given options argument is the same options dictionary passed to the self.reset function

Parameters:

options (dict) –

_sample_object_goal_poses(env_idx, difficulty)[source]#

Sample goal poses for the cube and sets them into the desired goal pose buffer.

Parameters:

  • instances – A tensor constraining indices of environment instances to reset.

  • difficulty (int) – Difficulty level. The higher, the more difficult is the goal.

  • env_idx (torch.Tensor) –

Possible levels are:

  • 0: Random goal position on the table, no orientation.

  • 1: Random goal position on the table, including yaw orientation.

  • 2: Fixed goal position in the air with x,y = 0. No orientation.

  • 3: Random goal position in the air, no orientation.

  • 4: Random goal pose in the air, including orientation.

compute_dense_reward(obs, action, info)[source]#

Compute the dense reward.

Parameters:

  • obs (Any) – The observation data. By default the observation data will be in its most raw form, a dictionary (no flattening, wrappers etc.)

  • action (torch.Tensor) – The most recent action.

  • info (dict) – The info dictionary.

compute_normalized_dense_reward(obs, action, info)[source]#

Compute the normalized dense reward.

Parameters:

  • obs (Any) – The observation data. By default the observation data will be in its most raw form, a dictionary (no flattening, wrappers etc.)

  • action (torch.Tensor) – The most recent action.

  • info (dict) – The info dictionary.

evaluate()[source]#

Evaluate whether the environment is currently in a success state by returning a dictionary with a “success” key or a failure state via a “fail” key

This function may also return additional data that has been computed (e.g. is the robot grasping some object) that may be reused when generating observations and rewards.

By default if not overriden this function returns an empty dictionary

ARENA_RADIUS = 0.195[source]#
SUPPORTED_ROBOTS = ['trifingerpro'][source]#

Override this to enforce which robots or tuples of robots together are supported in the task. During env creation, setting robot_uids auto loads all desired robots into the scene, but not all tasks are designed to support some robot setups

property _default_human_render_camera_configs[source]#

Add default cameras for rendering when using render_mode=’rgb_array’. These can be overriden by the user at env creation time

property _default_sensor_configs[source]#

Add default (non-agent) sensors to the environment by returning sensor configurations. These can be overriden by the user at env creation time

agent: mani_skill.agents.robots.TriFingerPro[source]#
cube_half_size = 0.02[source]#
difficulty_level = 4[source]#
goal_radius = 0.02[source]#
max_com_distance_to_center[source]#
max_height = 0.1[source]#
max_len = 0.065[source]#
min_height = 0.0325[source]#
radius_3d[source]#
robot_init_qpos_noise = 0.02[source]#
sim_config[source]#

the final sim config after merging user overrides with the environment default

size = 0.065[source]#
class mani_skill.envs.tasks.rotate_cube.RotateCubeEnvLevel0(*args, **kwargs)[source]#

Bases: RotateCubeEnv

Modified from NVIDIA-Omniverse/IsaacGymEnvs NVIDIA-Omniverse/IsaacGymEnvs

class mani_skill.envs.tasks.rotate_cube.RotateCubeEnvLevel1(*args, **kwargs)[source]#

Bases: RotateCubeEnv

Modified from NVIDIA-Omniverse/IsaacGymEnvs NVIDIA-Omniverse/IsaacGymEnvs

class mani_skill.envs.tasks.rotate_cube.RotateCubeEnvLevel2(*args, **kwargs)[source]#

Bases: RotateCubeEnv

Modified from NVIDIA-Omniverse/IsaacGymEnvs NVIDIA-Omniverse/IsaacGymEnvs

class mani_skill.envs.tasks.rotate_cube.RotateCubeEnvLevel3(*args, **kwargs)[source]#

Bases: RotateCubeEnv

Modified from NVIDIA-Omniverse/IsaacGymEnvs NVIDIA-Omniverse/IsaacGymEnvs

class mani_skill.envs.tasks.rotate_cube.RotateCubeEnvLevel4(*args, **kwargs)[source]#

Bases: RotateCubeEnv

Modified from NVIDIA-Omniverse/IsaacGymEnvs NVIDIA-Omniverse/IsaacGymEnvs