robomimic.envs package
Contents
robomimic.envs package#
Submodules#
robomimic.envs.env_base module#
This file contains the base class for environment wrappers that are used to provide a standardized environment API for training policies and interacting with metadata present in datasets.
- class robomimic.envs.env_base.EnvBase(env_name, render=False, render_offscreen=False, use_image_obs=False, postprocess_visual_obs=True, **kwargs)#
Bases:
abc.ABCA base class method for environments used by this repo.
- abstract property action_dimension#
Returns dimension of actions (int).
- abstract classmethod create_for_data_processing(camera_names, camera_height, camera_width, reward_shaping, **kwargs)#
Create environment for processing datasets, which includes extracting observations, labeling dense / sparse rewards, and annotating dones in transitions.
- Parameters
camera_names ([str]) – list of camera names that correspond to image observations
camera_height (int) – camera height for all cameras
camera_width (int) – camera width for all cameras
reward_shaping (bool) – if True, use shaped environment rewards, else use sparse task completion rewards
- Returns
env (EnvBase instance)
- abstract get_goal()#
Get goal observation. Not all environments support this.
- abstract get_observation()#
Get environment observation
- abstract get_reward()#
Get current reward.
- abstract get_state()#
Get environment simulator state, compatible with @reset_to
- abstract is_done()#
Check if the task is done (not necessarily successful).
- abstract is_success()#
Check if the task condition(s) is reached. Should return a dictionary { str: bool } with at least a “task” key for the overall task success, and additional optional keys corresponding to other task criteria.
- abstract property name#
Returns name of environment name (str).
- abstract render(mode='human', height=None, width=None, camera_name=None)#
Render
- abstract reset()#
Reset environment.
- Returns
initial observation dictionary.
- Return type
observation (dict)
- abstract reset_to(state)#
Reset to a specific simulator state.
- Parameters
state (dict) – current simulator state
- Returns
observation dictionary after setting the simulator state
- Return type
observation (dict)
- abstract property rollout_exceptions#
Return tuple of exceptions to except when doing rollouts. This is useful to ensure that the entire training run doesn’t crash because of a bad policy that causes unstable simulation computations.
- abstract serialize()#
Save all information needed to re-instantiate this environment in a dictionary. This is the same as @env_meta - environment metadata stored in hdf5 datasets, and used in utils/env_utils.py.
- abstract set_goal(**kwargs)#
Set goal observation with external specification. Not all environments support this.
- abstract step(action)#
Step in the environment with an action.
- Parameters
action (np.array) – action to take
- Returns
new observation dictionary reward (float): reward for this step done (bool): whether the task is done info (dict): extra information
- Return type
observation (dict)
- abstract property type#
Returns environment type (int) for this kind of environment. This helps identify this env class.
robomimic.envs.env_gym module#
robomimic.envs.env_ig_momart module#
robomimic.envs.env_robosuite module#
This file contains the robosuite environment wrapper that is used to provide a standardized environment API for training policies and interacting with metadata present in datasets.
- class robomimic.envs.env_robosuite.EnvRobosuite(env_name, render=False, render_offscreen=False, use_image_obs=False, postprocess_visual_obs=True, **kwargs)#
Bases:
robomimic.envs.env_base.EnvBaseWrapper class for robosuite environments (https://github.com/ARISE-Initiative/robosuite)
- property action_dimension#
Returns dimension of actions (int).
- classmethod create_for_data_processing(env_name, camera_names, camera_height, camera_width, reward_shaping, **kwargs)#
Create environment for processing datasets, which includes extracting observations, labeling dense / sparse rewards, and annotating dones in transitions.
- Parameters
env_name (str) – name of environment
camera_names (list of str) – list of camera names that correspond to image observations
camera_height (int) – camera height for all cameras
camera_width (int) – camera width for all cameras
reward_shaping (bool) – if True, use shaped environment rewards, else use sparse task completion rewards
- get_goal()#
Get goal observation. Not all environments support this.
- get_observation(di=None)#
Get current environment observation dictionary.
- Parameters
di (dict) – current raw observation dictionary from robosuite to wrap and provide as a dictionary. If not provided, will be queried from robosuite.
- get_reward()#
Get current reward.
- get_state()#
Get current environment simulator state as a dictionary. Should be compatible with @reset_to.
- is_done()#
Check if the task is done (not necessarily successful).
- is_success()#
Check if the task condition(s) is reached. Should return a dictionary { str: bool } with at least a “task” key for the overall task success, and additional optional keys corresponding to other task criteria.
- property name#
Returns name of environment name (str).
- render(mode='human', height=None, width=None, camera_name='agentview')#
Render from simulation to either an on-screen window or off-screen to RGB array.
- Parameters
mode (str) – pass “human” for on-screen rendering or “rgb_array” for off-screen rendering
height (int) – height of image to render - only used if mode is “rgb_array”
width (int) – width of image to render - only used if mode is “rgb_array”
camera_name (str) – camera name to use for rendering
- reset()#
Reset environment.
- Returns
initial observation dictionary.
- Return type
observation (dict)
- reset_to(state)#
Reset to a specific simulator state.
- Parameters
state (dict) – current simulator state that contains one or more of: - states (np.ndarray): initial state of the mujoco environment - model (str): mujoco scene xml
- Returns
- observation dictionary after setting the simulator state (only
if “states” is in @state)
- Return type
observation (dict)
- property rollout_exceptions#
Return tuple of exceptions to except when doing rollouts. This is useful to ensure that the entire training run doesn’t crash because of a bad policy that causes unstable simulation computations.
- serialize()#
Save all information needed to re-instantiate this environment in a dictionary. This is the same as @env_meta - environment metadata stored in hdf5 datasets, and used in utils/env_utils.py.
- set_goal(**kwargs)#
Set goal observation with external specification. Not all environments support this.
- step(action)#
Step in the environment with an action.
- Parameters
action (np.array) – action to take
- Returns
new observation dictionary reward (float): reward for this step done (bool): whether the task is done info (dict): extra information
- Return type
observation (dict)
- property type#
Returns environment type (int) for this kind of environment. This helps identify this env class.