# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md).
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
from __future__ import annotations
import inspect
import re
import weakref
from abc import ABC, abstractmethod
from collections.abc import Sequence
from typing import TYPE_CHECKING, Any
import torch
import warp as wp
import isaaclab.sim as sim_utils
from isaaclab.physics import PhysicsEvent, PhysicsManager
from isaaclab.sim.simulation_context import SimulationContext
from isaaclab.sim.utils.stage import get_current_stage
if TYPE_CHECKING:
from .asset_base_cfg import AssetBaseCfg
[docs]
class AssetBase(ABC):
"""The base interface class for assets.
An asset corresponds to any physics-enabled object that can be spawned in the simulation. These include
rigid objects, articulated objects, deformable objects etc. The core functionality of an asset is to
provide a set of buffers that can be used to interact with the simulator. The buffers are updated
by the asset class and can be written into the simulator using the their respective ``write`` methods.
This allows a convenient way to perform post-processing operations on the buffers before writing them
into the simulator and obtaining the corresponding simulation results.
The class handles both the spawning of the asset into the USD stage as well as initialization of necessary
physics handles to interact with the asset. Upon construction of the asset instance, the prim corresponding
to the asset is spawned into the USD stage if the spawn configuration is not None. The spawn configuration
is defined in the :attr:`AssetBaseCfg.spawn` attribute. In case the configured :attr:`AssetBaseCfg.prim_path`
is an expression, then the prim is spawned at all the matching paths. Otherwise, a single prim is spawned
at the configured path. For more information on the spawn configuration, see the
:mod:`isaaclab.sim.spawners` module.
Unlike backend-specific interfaces (e.g. Isaac Sim PhysX) where one usually needs to call
initialize explicitly, the asset class automatically initializes and invalidates physics
handles when the simulation is ready or stopped. This is done by registering callbacks
for the physics lifecycle events (:attr:`PhysicsEvent.PHYSICS_READY`, :attr:`PhysicsEvent.STOP`).
Additionally, the class registers a callback for debug visualization of the asset if a debug visualization
is implemented in the asset class. This can be enabled by setting the :attr:`AssetBaseCfg.debug_vis` attribute
to True. The debug visualization is implemented through the :meth:`_set_debug_vis_impl` and
:meth:`_debug_vis_callback` methods.
"""
[docs]
def __init__(self, cfg: AssetBaseCfg):
"""Initialize the asset base.
Args:
cfg: The configuration class for the asset.
Raises:
RuntimeError: If no prims found at input prim path or prim path expression.
"""
# check that the config is valid
cfg.validate()
# store inputs
self.cfg = cfg.copy()
# flag for whether the asset is initialized
self._is_initialized = False
# get stage handle
self.stage = get_current_stage()
# spawn the asset
# determine path where prims should exist after spawn
if self.cfg.spawn is not None:
# Use spawn_path if set (by InteractiveScene), otherwise fall back to prim_path
check_path = self.cfg.spawn.spawn_path if self.cfg.spawn.spawn_path is not None else self.cfg.prim_path
self.cfg.spawn.func(
check_path,
self.cfg.spawn,
translation=self.cfg.init_state.pos,
orientation=self.cfg.init_state.rot,
)
# check that prims exist
matching_prims = sim_utils.find_matching_prims(check_path)
if len(matching_prims) == 0:
raise RuntimeError(f"Could not find prim with path {check_path}.")
else:
# asset should exist at run time
check_path = self.cfg.prim_path
# register various callback functions
self._register_callbacks()
# add handle for debug visualization (this is set to a valid handle inside set_debug_vis)
self._debug_vis_handle = None
# set initial state of debug visualization
self.set_debug_vis(self.cfg.debug_vis)
def __del__(self):
"""Unsubscribe from the callbacks."""
# clear events handles
self._clear_callbacks()
"""
Properties
"""
@property
def is_initialized(self) -> bool:
"""Whether the asset is initialized.
Returns True if the asset is initialized, False otherwise.
"""
return self._is_initialized
@property
@abstractmethod
def num_instances(self) -> int:
"""Number of instances of the asset.
This is equal to the number of asset instances per environment multiplied by the number of environments.
"""
return NotImplementedError
@property
def device(self) -> str:
"""Memory device for computation."""
return self._device
@property
@abstractmethod
def data(self) -> Any:
"""Data related to the asset."""
return NotImplementedError
@property
def has_debug_vis_implementation(self) -> bool:
"""Whether the asset has a debug visualization implemented."""
# check if function raises NotImplementedError
source_code = inspect.getsource(self._set_debug_vis_impl)
return "NotImplementedError" not in source_code
"""
Operations.
"""
[docs]
def set_visibility(self, visible: bool, env_ids: Sequence[int] | None = None):
"""Set the visibility of the prims corresponding to the asset.
This operation affects the visibility of the prims corresponding to the asset in the USD stage.
It is useful for toggling the visibility of the asset in the simulator. For instance, one can
hide the asset when it is not being used to reduce the rendering overhead.
.. note::
This operation uses the PXR API to set the visibility of the prims. Thus, the operation
may have an overhead if the number of prims is large.
Args:
visible: Whether to make the prims visible or not.
env_ids: The indices of the object to set visibility. Defaults to None (all instances).
"""
# resolve the environment ids
if env_ids is None:
env_ids = range(len(self._prims))
elif isinstance(env_ids, torch.Tensor):
env_ids = env_ids.detach().cpu().tolist()
# obtain the prims corresponding to the asset
# note: we only want to find the prims once since this is a costly operation
if not hasattr(self, "_prims"):
self._prims = sim_utils.find_matching_prims(self.cfg.prim_path)
# iterate over the environment ids
for env_id in env_ids:
sim_utils.set_prim_visibility(self._prims[env_id], visible)
[docs]
def set_debug_vis(self, debug_vis: bool) -> bool:
"""Sets whether to visualize the asset data.
Args:
debug_vis: Whether to visualize the asset data.
Returns:
Whether the debug visualization was successfully set. False if the asset
does not support debug visualization.
"""
# check if debug visualization is supported
if not self.has_debug_vis_implementation:
return False
# toggle debug visualization objects
self._set_debug_vis_impl(debug_vis)
# toggle debug visualization handles (Kit/omni only for PhysX backend)
if debug_vis:
if self._debug_vis_handle is None:
sim_ctx = SimulationContext.instance()
if "physx" in sim_ctx.physics_manager.__name__.lower():
import omni.kit.app
app_interface = omni.kit.app.get_app_interface()
self._debug_vis_handle = app_interface.get_post_update_event_stream().create_subscription_to_pop(
lambda event, obj=weakref.proxy(self): obj._debug_vis_callback(event)
)
else:
if self._debug_vis_handle is not None:
self._debug_vis_handle.unsubscribe()
self._debug_vis_handle = None
# return success
return True
[docs]
@abstractmethod
def reset(self, env_ids: Sequence[int] | None = None):
"""Resets all internal buffers of selected environments.
Args:
env_ids: The indices of the object to reset. Defaults to None (all instances).
"""
raise NotImplementedError
[docs]
@abstractmethod
def write_data_to_sim(self):
"""Writes data to the simulator."""
raise NotImplementedError
[docs]
@abstractmethod
def update(self, dt: float):
"""Update the internal buffers.
The time step ``dt`` is used to compute numerical derivatives of quantities such as joint
accelerations which are not provided by the simulator.
Args:
dt: The amount of time passed from last ``update`` call.
"""
raise NotImplementedError
"""
Validation.
"""
# Mapping from warp dtype to the trailing dimensions that a torch.Tensor
# would have for the same data. Subclasses may extend this (e.g. custom
# ``vec6f`` in deformable objects) by updating the dict in their ``__init__``.
_DTYPE_TO_TORCH_TRAILING_DIMS: dict[type, tuple[int, ...]] = {
wp.float32: (),
wp.int32: (),
wp.vec2f: (2,),
wp.vec3f: (3,),
wp.vec4f: (4,),
wp.transformf: (7,),
wp.spatial_vectorf: (6,),
}
[docs]
def assert_shape_and_dtype(
self, tensor: float | torch.Tensor | wp.array, shape: tuple[int, ...], dtype: type, name: str = ""
) -> None:
"""Assert the shape and dtype of a tensor or warp array.
Args:
tensor: The tensor or warp array to assert the shape of. Floats are skipped.
shape: The expected leading dimensions (e.g. ``(num_envs, num_joints)``).
dtype: The expected warp dtype.
name: Optional parameter name for error messages.
"""
if __debug__:
cls = type(self).__name__
prefix = f"{cls}: '{name}' " if name else f"{cls}: "
if isinstance(tensor, (int, float)):
return
elif isinstance(tensor, wp.array):
assert tensor.dtype == dtype, f"{prefix}Dtype mismatch: {tensor.dtype} != {dtype}"
assert tensor.shape == shape, f"{prefix}Shape mismatch: {tensor.shape} != {shape}"
elif isinstance(tensor, torch.Tensor):
offset = self._DTYPE_TO_TORCH_TRAILING_DIMS.get(dtype)
if offset is None:
raise ValueError(f"Unsupported dtype: {dtype}")
assert tensor.shape == (*shape, *offset), (
f"{prefix}Shape mismatch: {tensor.shape} != {(*shape, *offset)}"
)
[docs]
def assert_shape_and_dtype_mask(
self,
tensor: float | torch.Tensor | wp.array,
masks: tuple[wp.array, ...],
dtype: type,
name: str = "",
trailing_dims: tuple[int, ...] = (),
) -> None:
"""Assert the shape of a tensor or warp array against mask dimensions.
Mask-based write methods expect **full-sized** data — one element per entry in each mask
dimension, regardless of how many entries are ``True``. The expected leading shape is therefore
``(mask_0.shape[0], mask_1.shape[0], ...)`` (i.e. the *total* size of each dimension, not the
number of selected entries).
Args:
tensor: The tensor or warp array to assert the shape of. Floats are skipped.
masks: Tuple of mask arrays whose ``shape[0]`` dimensions form the expected leading shape.
dtype: The expected warp dtype.
name: Optional parameter name for error messages.
trailing_dims: Extra trailing dimensions to append (e.g. ``(9,)`` for inertias with ``wp.float32``).
"""
if __debug__:
shape = (*tuple(m.shape[0] for m in masks), *trailing_dims)
self.assert_shape_and_dtype(tensor, shape, dtype, name)
"""
Implementation specific.
"""
@abstractmethod
def _initialize_impl(self):
"""Initializes the physics handles and internal buffers for the current backend."""
raise NotImplementedError
def _set_debug_vis_impl(self, debug_vis: bool):
"""Set debug visualization into visualization objects.
This function is responsible for creating the visualization objects if they don't exist
and input ``debug_vis`` is True. If the visualization objects exist, the function should
set their visibility into the stage.
"""
raise NotImplementedError(f"Debug visualization is not implemented for {self.__class__.__name__}.")
def _debug_vis_callback(self, event):
"""Callback for debug visualization.
This function calls the visualization objects and sets the data to visualize into them.
"""
raise NotImplementedError(f"Debug visualization is not implemented for {self.__class__.__name__}.")
"""
Internal simulation callbacks.
"""
def _register_callbacks(self):
"""Registers physics lifecycle callbacks via the current backend's physics manager."""
physics_mgr_cls = SimulationContext.instance().physics_manager
# note: use weakref on callbacks to ensure that this object can be deleted when its destructor is called.
obj_ref = weakref.proxy(self)
def _invoke(callback_name, event):
getattr(obj_ref, callback_name)(event)
# Backend-agnostic: PHYSICS_READY (init) and STOP (invalidate)
self._initialize_handle = physics_mgr_cls.register_callback(
lambda payload: PhysicsManager.safe_callback_invoke(
_invoke, "_initialize_callback", payload, physics_manager=physics_mgr_cls
),
PhysicsEvent.PHYSICS_READY,
order=10,
)
self._invalidate_initialize_handle = physics_mgr_cls.register_callback(
lambda payload: PhysicsManager.safe_callback_invoke(
_invoke, "_invalidate_initialize_callback", payload, physics_manager=physics_mgr_cls
),
PhysicsEvent.STOP,
order=10,
)
# Optional: prim deletion (only supported by PhysX backend)
self._prim_deletion_handle = None
physics_backend = physics_mgr_cls.__name__.lower()
if "physx" in physics_backend:
from isaaclab_physx.physics import IsaacEvents
self._prim_deletion_handle = physics_mgr_cls.register_callback(
lambda event: PhysicsManager.safe_callback_invoke(
_invoke, "_on_prim_deletion", event, physics_manager=physics_mgr_cls
),
IsaacEvents.PRIM_DELETION,
)
def _initialize_callback(self, event):
"""Initializes the scene elements.
.. note::
Physics handles are only valid once the simulation is ready. This callback runs when
:attr:`PhysicsEvent.PHYSICS_READY` is dispatched by the current backend.
"""
if not self._is_initialized:
self._backend = SimulationContext.instance().physics_manager.get_backend()
self._device = SimulationContext.instance().physics_manager.get_device()
self._initialize_impl()
self._is_initialized = True
def _invalidate_initialize_callback(self, event):
"""Invalidates the scene elements."""
self._is_initialized = False
if self._debug_vis_handle is not None:
self._debug_vis_handle.unsubscribe()
self._debug_vis_handle = None
def _on_prim_deletion(self, event) -> None:
"""Invalidates and clears callbacks when the prim is deleted.
Only used when the backend supports prim deletion events (e.g. PhysX).
"""
payload = getattr(event, "payload", event) if not isinstance(event, dict) else event
prim_path = payload.get("prim_path", "") if isinstance(payload, dict) else ""
if prim_path == "/":
self._clear_callbacks()
return
result = re.match(
pattern="^" + "/".join(self.cfg.prim_path.split("/")[: prim_path.count("/") + 1]) + "$", string=prim_path
)
if result:
self._clear_callbacks()
def _clear_callbacks(self) -> None:
"""Clears all registered callbacks."""
if self._initialize_handle is not None:
self._initialize_handle.deregister()
self._initialize_handle = None
if self._invalidate_initialize_handle is not None:
self._invalidate_initialize_handle.deregister()
self._invalidate_initialize_handle = None
if self._prim_deletion_handle is not None:
self._prim_deletion_handle.deregister()
self._prim_deletion_handle = None
if self._debug_vis_handle is not None:
self._debug_vis_handle.unsubscribe()
self._debug_vis_handle = None
