mani_skill.envs.tasks.digital_twins.so100_arm ============================================= .. py:module:: mani_skill.envs.tasks.digital_twins.so100_arm Submodules ---------- .. toctree:: :maxdepth: 1 /api/mani_skill/envs/tasks/digital_twins/so100_arm/grasp_cube/index Classes ------- .. autoapisummary:: mani_skill.envs.tasks.digital_twins.so100_arm.SO100GraspCubeEnv Package Contents ---------------- .. py:class:: SO100GraspCubeEnv(*args, robot_uids='so100', control_mode='pd_joint_target_delta_pos', greenscreen_overlay_path=None, domain_randomization_config = SO100GraspCubeDomainRandomizationConfig(), domain_randomization=True, base_camera_settings=dict(fov=52 * np.pi / 180, pos=[0.5, 0.3, 0.35], target=[0.3, 0.0, 0.1]), spawn_box_pos=[0.3, 0.05], spawn_box_half_size=0.2 / 2, **kwargs) Bases: :py:obj:`mani_skill.envs.tasks.digital_twins.base_env.BaseDigitalTwinEnv` **Task Description:** A simple task where the objective is to grasp a cube with the SO100 arm and bring it up to a target rest pose. **Randomizations:** - the cube's xy position is randomized on top of a table in a region of size [0.2, 0.2] x [-0.2, -0.2]. It is placed flat on the table - the cube's z-axis rotation is randomized to a random angle **Success Conditions:** - the cube is lifted, grasped, and the robot returns to a rest pose above the surface of the table .. py:method:: _before_control_step() Code that runs before each action has been taken via env.step(action). On GPU simulation this is called before observations are fetched from the GPU buffers. .. py:method:: _get_obs_agent() Get observations about the agent's state. By default it is proprioceptive observations which include qpos and qvel. Controller state is also included although most default controllers do not have any state. .. py:method:: _get_obs_extra(info) Get task-relevant extra observations. Usually defined on a task by task basis .. py:method:: _initialize_episode(env_idx, options) Initialize the episode, e.g., poses of actors and articulations, as well as task relevant data like randomizing goal positions .. py:method:: _load_agent(options) loads the agent/controllable articulations into the environment. The default function provides a convenient way to setup the agent/robot by a robot_uid (stored in self.robot_uids) without requiring the user to have to write the robot building and controller code themselves. For more advanced use-cases you can override this function to have more control over the agent/robot building process. :param options: The options for the environment. :type options: dict :param initial_agent_poses: The initial poses of the agent/robot. Providing these poses and ensuring they are picked such that they do not collide with objects if spawned there is highly recommended to ensure more stable simulation (the agent pose can be changed later during episode initialization). :type initial_agent_poses: Optional[Union[sapien.Pose, Pose]] :param build_separate: Whether to build the agent/robot separately. If True, the agent/robot will be built separately for each parallel environment and then merged together to be accessible under one view/object. This is useful for randomizing physical and visual properties of the agent/robot which is only permitted for articulations built separately in each environment. :type build_separate: bool .. py:method:: _load_lighting(options) Loads lighting into the scene. Called by `self._reconfigure`. If not overriden will set some simple default lighting .. py:method:: _load_scene(options) Load assets for a digital twin scene in .. py:method:: compute_dense_reward(obs, action, info) Compute the dense reward. :param obs: The observation data. By default the observation data will be in its most raw form, a dictionary (no flattening, wrappers etc.) :type obs: Any :param action: The most recent action. :type action: torch.Tensor :param info: The info dictionary. :type info: dict .. py:method:: compute_normalized_dense_reward(obs, action, info) Compute the normalized dense reward. :param obs: The observation data. By default the observation data will be in its most raw form, a dictionary (no flattening, wrappers etc.) :type obs: Any :param action: The most recent action. :type action: torch.Tensor :param info: The info dictionary. :type info: dict .. py:method:: evaluate() 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 .. py:method:: sample_camera_poses(n) .. py:attribute:: SUPPORTED_OBS_MODES :value: ['none', 'state', 'state_dict', 'rgb+segmentation'] The string observation modes the environment supports. Note that "none" and "any_texture" are special keys. none indicates no observation data is generated. "any_texture" indicates that any combination of image textures generated by cameras are supported e.g. rgb+depth, normal+segmentation, albedo+rgb+depth etc. For a full list of supported textures see .. py:attribute:: SUPPORTED_ROBOTS :value: ['so100'] 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 .. py:property:: _default_human_render_camera_configs Add default cameras for rendering when using render_mode='rgb_array'. These can be overriden by the user at env creation time .. py:property:: _default_sensor_configs Add default (non-agent) sensors to the environment by returning sensor configurations. These can be overriden by the user at env creation time .. py:property:: _default_sim_config .. py:attribute:: agent :type: mani_skill.agents.robots.so100.so_100.SO100 .. py:attribute:: base_camera_settings what the camera fov, position and target are when domain randomization is off. DR is centered around these settings .. py:attribute:: domain_randomization :value: True whether randomization is turned on or off. .. py:attribute:: domain_randomization_config domain randomization config .. py:attribute:: spawn_box_half_size :value: 0.1 .. py:attribute:: spawn_box_pos :value: [0.3, 0.05]