mani_skill.envs.tasks.tabletop.lift_peg_upright
===============================================

.. py:module:: mani_skill.envs.tasks.tabletop.lift_peg_upright


Classes
-------

.. autoapisummary::

   mani_skill.envs.tasks.tabletop.lift_peg_upright.LiftPegUprightEnv


Module Contents
---------------

.. py:class:: LiftPegUprightEnv(*args, robot_uids='panda', robot_init_qpos_noise=0.02, **kwargs)

   Bases: :py:obj:`mani_skill.envs.sapien_env.BaseEnv`


   **Task Description:**
   A simple task where the objective is to move a peg laying on the table to any upright position on the table

   **Randomizations:**
   - the peg's xy position is randomized on top of a table in the region [0.1, 0.1] x [-0.1, -0.1]. It is placed flat along it's length on the table

   **Success Conditions:**
   - the absolute value of the peg's y euler angle is within 0.08 of $\pi$/2 and the z position of the peg is within 0.005 of its half-length (0.12).


   .. 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_scene(options)

      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



   .. 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:attribute:: SUPPORTED_ROBOTS
      :value: ['panda', 'fetch']


      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:attribute:: _sample_video_link
      :value: 'https://github.com/haosulab/ManiSkill/raw/main/figures/environment_demos/LiftPegUpright-v1_rt.mp4'


      a link to a sample video of the task. This is mostly used for automatic documentation generation


   .. py:attribute:: agent
      :type:  Union[mani_skill.agents.robots.Panda, mani_skill.agents.robots.Fetch]


   .. py:attribute:: peg_half_length
      :value: 0.12



   .. py:attribute:: peg_half_width
      :value: 0.025



   .. py:attribute:: robot_init_qpos_noise
      :value: 0.02