mani_skill.envs.tasks.tabletop.push_cube#
Code for a minimal environment/task with just a robot being loaded. We recommend copying this template and modifying as you need.
At a high-level, ManiSkill tasks can minimally be defined by how the environment resets, what agents/objects are loaded, goal parameterization, and success conditions
Environment reset is comprised of running two functions, self._reconfigure and self.initialize_episode, which is auto run by ManiSkill. As a user, you can override a number of functions that affect reconfiguration and episode initialization.
Reconfiguration will reset the entire environment scene and allow you to load/swap assets and agents.
Episode initialization will reset the positions of all objects (called actors), articulations, and agents, in addition to initializing any task relevant data like a goal
See comments for how to make your own environment and what each required function should do
Classes#
Task Description: |
Module Contents#
- class mani_skill.envs.tasks.tabletop.push_cube.PushCubeEnv(*args, robot_uids='panda', robot_init_qpos_noise=0.02, **kwargs)[source]#
Bases:
mani_skill.envs.sapien_env.BaseEnvTask Description: A simple task where the objective is to push and move a cube to a goal region in front of it
Randomizations: - the cube’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 on the table - the target goal region is marked by a red/white circular target. The position of the target is fixed to be the cube xy position + [0.1 + goal_radius, 0]
Success Conditions: - the cube’s xy position is within goal_radius (default 0.1) of the target’s xy position by euclidean distance and the cube is still on the table.
- _get_obs_extra(info)[source]#
Get task-relevant extra observations. Usually defined on a task by task basis
- Parameters:
info (dict) –
- _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_agent(options)[source]#
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.
- Parameters:
options (dict) – The options for the environment.
initial_agent_poses (Optional[Union[sapien.Pose, Pose]]) – 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).
build_separate (bool) – 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.
- _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) –
- 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
- SUPPORTED_ROBOTS = ['panda', 'fetch'][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