Source code for omni.isaac.lab.envs.manager_based_env

# Copyright (c) 2022-2024, The Isaac Lab Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause

import builtins
import torch
from collections.abc import Sequence
from typing import Any

import omni.isaac.core.utils.torch as torch_utils
import omni.log

from omni.isaac.lab.managers import ActionManager, EventManager, ObservationManager
from omni.isaac.lab.scene import InteractiveScene
from omni.isaac.lab.sim import SimulationContext
from omni.isaac.lab.utils.timer import Timer

from .common import VecEnvObs
from .manager_based_env_cfg import ManagerBasedEnvCfg
from .ui import ViewportCameraController


[docs]class ManagerBasedEnv: """The base environment encapsulates the simulation scene and the environment managers for the manager-based workflow. While a simulation scene or world comprises of different components such as the robots, objects, and sensors (cameras, lidars, etc.), the environment is a higher level abstraction that provides an interface for interacting with the simulation. The environment is comprised of the following components: * **Scene**: The scene manager that creates and manages the virtual world in which the robot operates. This includes defining the robot, static and dynamic objects, sensors, etc. * **Observation Manager**: The observation manager that generates observations from the current simulation state and the data gathered from the sensors. These observations may include privileged information that is not available to the robot in the real world. Additionally, user-defined terms can be added to process the observations and generate custom observations. For example, using a network to embed high-dimensional observations into a lower-dimensional space. * **Action Manager**: The action manager that processes the raw actions sent to the environment and converts them to low-level commands that are sent to the simulation. It can be configured to accept raw actions at different levels of abstraction. For example, in case of a robotic arm, the raw actions can be joint torques, joint positions, or end-effector poses. Similarly for a mobile base, it can be the joint torques, or the desired velocity of the floating base. * **Event Manager**: The event manager orchestrates operations triggered based on simulation events. This includes resetting the scene to a default state, applying random pushes to the robot at different intervals of time, or randomizing properties such as mass and friction coefficients. This is useful for training and evaluating the robot in a variety of scenarios. The environment provides a unified interface for interacting with the simulation. However, it does not include task-specific quantities such as the reward function, or the termination conditions. These quantities are often specific to defining Markov Decision Processes (MDPs) while the base environment is agnostic to the MDP definition. The environment steps forward in time at a fixed time-step. The physics simulation is decimated at a lower time-step. This is to ensure that the simulation is stable. These two time-steps can be configured independently using the :attr:`ManagerBasedEnvCfg.decimation` (number of simulation steps per environment step) and the :attr:`ManagerBasedEnvCfg.sim.dt` (physics time-step) parameters. Based on these parameters, the environment time-step is computed as the product of the two. The two time-steps can be obtained by querying the :attr:`physics_dt` and the :attr:`step_dt` properties respectively. """
[docs] def __init__(self, cfg: ManagerBasedEnvCfg): """Initialize the environment. Args: cfg: The configuration object for the environment. Raises: RuntimeError: If a simulation context already exists. The environment must always create one since it configures the simulation context and controls the simulation. """ # check that the config is valid cfg.validate() # store inputs to class self.cfg = cfg # initialize internal variables self._is_closed = False # set the seed for the environment if self.cfg.seed is not None: self.cfg.seed = self.seed(self.cfg.seed) else: omni.log.warn("Seed not set for the environment. The environment creation may not be deterministic.") # create a simulation context to control the simulator if SimulationContext.instance() is None: # the type-annotation is required to avoid a type-checking error # since it gets confused with Isaac Sim's SimulationContext class self.sim: SimulationContext = SimulationContext(self.cfg.sim) else: # simulation context should only be created before the environment # when in extension mode if not builtins.ISAAC_LAUNCHED_FROM_TERMINAL: raise RuntimeError("Simulation context already exists. Cannot create a new one.") self.sim: SimulationContext = SimulationContext.instance() # print useful information print("[INFO]: Base environment:") print(f"\tEnvironment device : {self.device}") print(f"\tEnvironment seed : {self.cfg.seed}") print(f"\tPhysics step-size : {self.physics_dt}") print(f"\tRendering step-size : {self.physics_dt * self.cfg.sim.render_interval}") print(f"\tEnvironment step-size : {self.step_dt}") if self.cfg.sim.render_interval < self.cfg.decimation: msg = ( f"The render interval ({self.cfg.sim.render_interval}) is smaller than the decimation " f"({self.cfg.decimation}). Multiple render calls will happen for each environment step. " "If this is not intended, set the render interval to be equal to the decimation." ) omni.log.warn(msg) # counter for simulation steps self._sim_step_counter = 0 # generate scene with Timer("[INFO]: Time taken for scene creation", "scene_creation"): self.scene = InteractiveScene(self.cfg.scene) print("[INFO]: Scene manager: ", self.scene) # set up camera viewport controller # viewport is not available in other rendering modes so the function will throw a warning # FIXME: This needs to be fixed in the future when we unify the UI functionalities even for # non-rendering modes. if self.sim.render_mode >= self.sim.RenderMode.PARTIAL_RENDERING: self.viewport_camera_controller = ViewportCameraController(self, self.cfg.viewer) else: self.viewport_camera_controller = None # play the simulator to activate physics handles # note: this activates the physics simulation view that exposes TensorAPIs # note: when started in extension mode, first call sim.reset_async() and then initialize the managers if builtins.ISAAC_LAUNCHED_FROM_TERMINAL is False: print("[INFO]: Starting the simulation. This may take a few seconds. Please wait...") with Timer("[INFO]: Time taken for simulation start", "simulation_start"): self.sim.reset() # add timeline event to load managers self.load_managers() # make sure torch is running on the correct device if "cuda" in self.device: torch.cuda.set_device(self.device) # extend UI elements # we need to do this here after all the managers are initialized # this is because they dictate the sensors and commands right now if self.sim.has_gui() and self.cfg.ui_window_class_type is not None: self._window = self.cfg.ui_window_class_type(self, window_name="IsaacLab") else: # if no window, then we don't need to store the window self._window = None # allocate dictionary to store metrics self.extras = {}
def __del__(self): """Cleanup for the environment.""" self.close() """ Properties. """ @property def num_envs(self) -> int: """The number of instances of the environment that are running.""" return self.scene.num_envs @property def physics_dt(self) -> float: """The physics time-step (in s). This is the lowest time-decimation at which the simulation is happening. """ return self.cfg.sim.dt @property def step_dt(self) -> float: """The environment stepping time-step (in s). This is the time-step at which the environment steps forward. """ return self.cfg.sim.dt * self.cfg.decimation @property def device(self): """The device on which the environment is running.""" return self.sim.device """ Operations - Setup. """
[docs] def load_managers(self): """Load the managers for the environment. This function is responsible for creating the various managers (action, observation, events, etc.) for the environment. Since the managers require access to physics handles, they can only be created after the simulator is reset (i.e. played for the first time). .. note:: In case of standalone application (when running simulator from Python), the function is called automatically when the class is initialized. However, in case of extension mode, the user must call this function manually after the simulator is reset. This is because the simulator is only reset when the user calls :meth:`SimulationContext.reset_async` and it isn't possible to call async functions in the constructor. """ # prepare the managers # -- action manager self.action_manager = ActionManager(self.cfg.actions, self) print("[INFO] Action Manager: ", self.action_manager) # -- observation manager self.observation_manager = ObservationManager(self.cfg.observations, self) print("[INFO] Observation Manager:", self.observation_manager) # -- event manager self.event_manager = EventManager(self.cfg.events, self) print("[INFO] Event Manager: ", self.event_manager) # perform events at the start of the simulation # in-case a child implementation creates other managers, the randomization should happen # when all the other managers are created if self.__class__ == ManagerBasedEnv and "startup" in self.event_manager.available_modes: self.event_manager.apply(mode="startup")
""" Operations - MDP. """
[docs] def reset(self, seed: int | None = None, options: dict[str, Any] | None = None) -> tuple[VecEnvObs, dict]: """Resets all the environments and returns observations. This function calls the :meth:`_reset_idx` function to reset all the environments. However, certain operations, such as procedural terrain generation, that happened during initialization are not repeated. Args: seed: The seed to use for randomization. Defaults to None, in which case the seed is not set. options: Additional information to specify how the environment is reset. Defaults to None. Note: This argument is used for compatibility with Gymnasium environment definition. Returns: A tuple containing the observations and extras. """ # set the seed if seed is not None: self.seed(seed) # reset state of scene indices = torch.arange(self.num_envs, dtype=torch.int64, device=self.device) self._reset_idx(indices) # if sensors are added to the scene, make sure we render to reflect changes in reset if self.sim.has_rtx_sensors() and self.cfg.rerender_on_reset: self.sim.render() # return observations return self.observation_manager.compute(), self.extras
[docs] def step(self, action: torch.Tensor) -> tuple[VecEnvObs, dict]: """Execute one time-step of the environment's dynamics. The environment steps forward at a fixed time-step, while the physics simulation is decimated at a lower time-step. This is to ensure that the simulation is stable. These two time-steps can be configured independently using the :attr:`ManagerBasedEnvCfg.decimation` (number of simulation steps per environment step) and the :attr:`ManagerBasedEnvCfg.sim.dt` (physics time-step). Based on these parameters, the environment time-step is computed as the product of the two. Args: action: The actions to apply on the environment. Shape is (num_envs, action_dim). Returns: A tuple containing the observations and extras. """ # process actions self.action_manager.process_action(action.to(self.device)) # check if we need to do rendering within the physics loop # note: checked here once to avoid multiple checks within the loop is_rendering = self.sim.has_gui() or self.sim.has_rtx_sensors() # perform physics stepping for _ in range(self.cfg.decimation): self._sim_step_counter += 1 # set actions into buffers self.action_manager.apply_action() # set actions into simulator self.scene.write_data_to_sim() # simulate self.sim.step(render=False) # render between steps only if the GUI or an RTX sensor needs it # note: we assume the render interval to be the shortest accepted rendering interval. # If a camera needs rendering at a faster frequency, this will lead to unexpected behavior. if self._sim_step_counter % self.cfg.sim.render_interval == 0 and is_rendering: self.sim.render() # update buffers at sim dt self.scene.update(dt=self.physics_dt) # post-step: step interval event if "interval" in self.event_manager.available_modes: self.event_manager.apply(mode="interval", dt=self.step_dt) # return observations and extras return self.observation_manager.compute(), self.extras
[docs] @staticmethod def seed(seed: int = -1) -> int: """Set the seed for the environment. Args: seed: The seed for random generator. Defaults to -1. Returns: The seed used for random generator. """ # set seed for replicator try: import omni.replicator.core as rep rep.set_global_seed(seed) except ModuleNotFoundError: pass # set seed for torch and other libraries return torch_utils.set_seed(seed)
[docs] def close(self): """Cleanup for the environment.""" if not self._is_closed: # destructor is order-sensitive del self.viewport_camera_controller del self.action_manager del self.observation_manager del self.event_manager del self.scene # clear callbacks and instance self.sim.clear_all_callbacks() self.sim.clear_instance() # destroy the window if self._window is not None: self._window = None # update closing status self._is_closed = True
""" Helper functions. """ def _reset_idx(self, env_ids: Sequence[int]): """Reset environments based on specified indices. Args: env_ids: List of environment ids which must be reset """ # reset the internal buffers of the scene elements self.scene.reset(env_ids) # apply events such as randomization for environments that need a reset if "reset" in self.event_manager.available_modes: env_step_count = self._sim_step_counter // self.cfg.decimation self.event_manager.apply(mode="reset", env_ids=env_ids, global_env_step_count=env_step_count) # iterate over all managers and reset them # this returns a dictionary of information which is stored in the extras # note: This is order-sensitive! Certain things need be reset before others. self.extras["log"] = dict() # -- observation manager info = self.observation_manager.reset(env_ids) self.extras["log"].update(info) # -- action manager info = self.action_manager.reset(env_ids) self.extras["log"].update(info) # -- event manager info = self.event_manager.reset(env_ids) self.extras["log"].update(info)