igibson.tasks package

Submodules

igibson.tasks.bddl_backend module

igibson.tasks.behavior_task module

igibson.tasks.dummy_task module

class igibson.tasks.dummy_task.DummyTask(env)

Bases: igibson.tasks.task_base.BaseTask

Point Nav Fixed Task The goal is to navigate to a fixed goal position

get_task_obs(env)

Get task-specific observation, including goal position, current velocities, etc.

Parameters: env – environment instance
Returns: task-specific observation

reset_agent(env)

Task-specific agent reset: land the robot to initial pose, compute initial potential

Parameters: env – environment instance

reset_scene(env)

Task-specific scene reset: reset scene objects or floor plane

Parameters: env – environment instance

igibson.tasks.dynamic_nav_random_task module

igibson.tasks.interactive_nav_random_task module

igibson.tasks.point_nav_fixed_task module

igibson.tasks.point_nav_random_task module

igibson.tasks.reaching_random_task module

igibson.tasks.room_rearrangement_task module

igibson.tasks.task_base module

class igibson.tasks.task_base.BaseTask(env)

Bases: object

Base Task class. Task-specific reset_scene, reset_agent, get_task_obs, step methods are implemented in subclasses Subclasses are expected to populate self.reward_functions and self.termination_conditions

get_reward(env, collision_links=[], action=None, info={})

Aggreate reward functions

Parameters

env – environment instance
collision_links – collision links after executing action
action – the executed action
info – additional info

Return reward

total reward of the current timestep

Return info

additional info

abstract get_task_obs(env)

Get task-specific observation

Parameters: env – environment instance
Returns: task-specific observation (numpy array)

get_termination(env, collision_links=[], action=None, info={})

Aggreate termination conditions

Parameters

env – environment instance
collision_links – collision links after executing action
action – the executed action
info – additional info

Return done

whether the episode has terminated

Return info

additional info

reset(env)

abstract reset_agent(env)

Task-specific agent reset

Parameters: env – environment instance

abstract reset_scene(env)

Task-specific scene reset

Parameters: env – environment instance

reset_variables(env)

Task-specific variable reset

Parameters: env – environment instance

step(env)

Perform task-specific step for every timestep

Parameters: env – environment instance