# Copyright (c) 2022-2024, The Isaac Lab Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
"""Observation manager for computing observation signals for a given world."""
from __future__ import annotations
import inspect
import numpy as np
import torch
from collections.abc import Sequence
from prettytable import PrettyTable
from typing import TYPE_CHECKING
from omni.isaac.lab.utils import modifiers
from omni.isaac.lab.utils.buffers import CircularBuffer
from .manager_base import ManagerBase, ManagerTermBase
from .manager_term_cfg import ObservationGroupCfg, ObservationTermCfg
if TYPE_CHECKING:
from omni.isaac.lab.envs import ManagerBasedEnv
[docs]class ObservationManager(ManagerBase):
"""Manager for computing observation signals for a given world.
Observations are organized into groups based on their intended usage. This allows having different observation
groups for different types of learning such as asymmetric actor-critic and student-teacher training. Each
group contains observation terms which contain information about the observation function to call, the noise
corruption model to use, and the sensor to retrieve data from.
Each observation group should inherit from the :class:`ObservationGroupCfg` class. Within each group, each
observation term should instantiate the :class:`ObservationTermCfg` class. Based on the configuration, the
observations in a group can be concatenated into a single tensor or returned as a dictionary with keys
corresponding to the term's name.
If the observations in a group are concatenated, the shape of the concatenated tensor is computed based on the
shapes of the individual observation terms. This information is stored in the :attr:`group_obs_dim` dictionary
with keys as the group names and values as the shape of the observation tensor. When the terms in a group are not
concatenated, the attribute stores a list of shapes for each term in the group.
.. note::
When the observation terms in a group do not have the same shape, the observation terms cannot be
concatenated. In this case, please set the :attr:`ObservationGroupCfg.concatenate_terms` attribute in the
group configuration to False.
Observations can also have history. This means a running history is updated per sim step. History can be controlled
per :class:`ObservationTermCfg` (See the :attr:`ObservationTermCfg.history_length` and
:attr:`ObservationTermCfg.flatten_history_dim`). History can also be controlled via :class:`ObservationGroupCfg`
where group configuration overwrites per term configuration if set. History follows an oldest to newest ordering.
The observation manager can be used to compute observations for all the groups or for a specific group. The
observations are computed by calling the registered functions for each term in the group. The functions are
called in the order of the terms in the group. The functions are expected to return a tensor with shape
(num_envs, ...).
If a noise model or custom modifier is registered for a term, the function is called to corrupt
the observation. The corruption function is expected to return a tensor with the same shape as the observation.
The observations are clipped and scaled as per the configuration settings.
"""
[docs] def __init__(self, cfg: object, env: ManagerBasedEnv):
"""Initialize observation manager.
Args:
cfg: The configuration object or dictionary (``dict[str, ObservationGroupCfg]``).
env: The environment instance.
Raises:
ValueError: If the configuration is None.
RuntimeError: If the shapes of the observation terms in a group are not compatible for concatenation
and the :attr:`~ObservationGroupCfg.concatenate_terms` attribute is set to True.
"""
# check that cfg is not None
if cfg is None:
raise ValueError("Observation manager configuration is None. Please provide a valid configuration.")
# call the base class constructor (this will parse the terms config)
super().__init__(cfg, env)
# compute combined vector for obs group
self._group_obs_dim: dict[str, tuple[int, ...] | list[tuple[int, ...]]] = dict()
for group_name, group_term_dims in self._group_obs_term_dim.items():
# if terms are concatenated, compute the combined shape into a single tuple
# otherwise, keep the list of shapes as is
if self._group_obs_concatenate[group_name]:
try:
term_dims = [torch.tensor(dims, device="cpu") for dims in group_term_dims]
self._group_obs_dim[group_name] = tuple(torch.sum(torch.stack(term_dims, dim=0), dim=0).tolist())
except RuntimeError:
raise RuntimeError(
f"Unable to concatenate observation terms in group '{group_name}'."
f" The shapes of the terms are: {group_term_dims}."
" Please ensure that the shapes are compatible for concatenation."
" Otherwise, set 'concatenate_terms' to False in the group configuration."
)
else:
self._group_obs_dim[group_name] = group_term_dims
# Stores the latest observations.
self._obs_buffer: dict[str, torch.Tensor | dict[str, torch.Tensor]] | None = None
def __str__(self) -> str:
"""Returns: A string representation for the observation manager."""
msg = f"<ObservationManager> contains {len(self._group_obs_term_names)} groups.\n"
# add info for each group
for group_name, group_dim in self._group_obs_dim.items():
# create table for term information
table = PrettyTable()
table.title = f"Active Observation Terms in Group: '{group_name}'"
if self._group_obs_concatenate[group_name]:
table.title += f" (shape: {group_dim})"
table.field_names = ["Index", "Name", "Shape"]
# set alignment of table columns
table.align["Name"] = "l"
# add info for each term
obs_terms = zip(
self._group_obs_term_names[group_name],
self._group_obs_term_dim[group_name],
)
for index, (name, dims) in enumerate(obs_terms):
# resolve inputs to simplify prints
tab_dims = tuple(dims)
# add row
table.add_row([index, name, tab_dims])
# convert table to string
msg += table.get_string()
msg += "\n"
return msg
[docs] def get_active_iterable_terms(self, env_idx: int) -> Sequence[tuple[str, Sequence[float]]]:
"""Returns the active terms as iterable sequence of tuples.
The first element of the tuple is the name of the term and the second element is the raw value(s) of the term.
Args:
env_idx: The specific environment to pull the active terms from.
Returns:
The active terms.
"""
terms = []
if self._obs_buffer is None:
self.compute()
obs_buffer: dict[str, torch.Tensor | dict[str, torch.Tensor]] = self._obs_buffer
for group_name, _ in self._group_obs_dim.items():
if not self.group_obs_concatenate[group_name]:
for name, term in obs_buffer[group_name].items():
terms.append((group_name + "-" + name, term[env_idx].cpu().tolist()))
continue
idx = 0
# add info for each term
data = obs_buffer[group_name]
for name, shape in zip(
self._group_obs_term_names[group_name],
self._group_obs_term_dim[group_name],
):
data_length = np.prod(shape)
term = data[env_idx, idx : idx + data_length]
terms.append((group_name + "-" + name, term.cpu().tolist()))
idx += data_length
return terms
"""
Properties.
"""
@property
def active_terms(self) -> dict[str, list[str]]:
"""Name of active observation terms in each group.
The keys are the group names and the values are the list of observation term names in the group.
"""
return self._group_obs_term_names
@property
def group_obs_dim(self) -> dict[str, tuple[int, ...] | list[tuple[int, ...]]]:
"""Shape of computed observations in each group.
The key is the group name and the value is the shape of the observation tensor.
If the terms in the group are concatenated, the value is a single tuple representing the
shape of the concatenated observation tensor. Otherwise, the value is a list of tuples,
where each tuple represents the shape of the observation tensor for a term in the group.
"""
return self._group_obs_dim
@property
def group_obs_term_dim(self) -> dict[str, list[tuple[int, ...]]]:
"""Shape of individual observation terms in each group.
The key is the group name and the value is a list of tuples representing the shape of the observation terms
in the group. The order of the tuples corresponds to the order of the terms in the group.
This matches the order of the terms in the :attr:`active_terms`.
"""
return self._group_obs_term_dim
@property
def group_obs_concatenate(self) -> dict[str, bool]:
"""Whether the observation terms are concatenated in each group or not.
The key is the group name and the value is a boolean specifying whether the observation terms in the group
are concatenated into a single tensor. If True, the observations are concatenated along the last dimension.
The values are set based on the :attr:`~ObservationGroupCfg.concatenate_terms` attribute in the group
configuration.
"""
return self._group_obs_concatenate
"""
Operations.
"""
[docs] def reset(self, env_ids: Sequence[int] | None = None) -> dict[str, float]:
# call all terms that are classes
for group_name, group_cfg in self._group_obs_class_term_cfgs.items():
for term_cfg in group_cfg:
term_cfg.func.reset(env_ids=env_ids)
# reset terms with history
for term_name in self._group_obs_term_names[group_name]:
if term_name in self._group_obs_term_history_buffer[group_name]:
self._group_obs_term_history_buffer[group_name][term_name].reset(batch_ids=env_ids)
# call all modifiers that are classes
for mod in self._group_obs_class_modifiers:
mod.reset(env_ids=env_ids)
# nothing to log here
return {}
[docs] def compute(self) -> dict[str, torch.Tensor | dict[str, torch.Tensor]]:
"""Compute the observations per group for all groups.
The method computes the observations for all the groups handled by the observation manager.
Please check the :meth:`compute_group` on the processing of observations per group.
Returns:
A dictionary with keys as the group names and values as the computed observations.
The observations are either concatenated into a single tensor or returned as a dictionary
with keys corresponding to the term's name.
"""
# create a buffer for storing obs from all the groups
obs_buffer = dict()
# iterate over all the terms in each group
for group_name in self._group_obs_term_names:
obs_buffer[group_name] = self.compute_group(group_name)
# otherwise return a dict with observations of all groups
# Cache the observations.
self._obs_buffer = obs_buffer
return obs_buffer
[docs] def compute_group(self, group_name: str) -> torch.Tensor | dict[str, torch.Tensor]:
"""Computes the observations for a given group.
The observations for a given group are computed by calling the registered functions for each
term in the group. The functions are called in the order of the terms in the group. The functions
are expected to return a tensor with shape (num_envs, ...).
The following steps are performed for each observation term:
1. Compute observation term by calling the function
2. Apply custom modifiers in the order specified in :attr:`ObservationTermCfg.modifiers`
3. Apply corruption/noise model based on :attr:`ObservationTermCfg.noise`
4. Apply clipping based on :attr:`ObservationTermCfg.clip`
5. Apply scaling based on :attr:`ObservationTermCfg.scale`
We apply noise to the computed term first to maintain the integrity of how noise affects the data
as it truly exists in the real world. If the noise is applied after clipping or scaling, the noise
could be artificially constrained or amplified, which might misrepresent how noise naturally occurs
in the data.
Args:
group_name: The name of the group for which to compute the observations. Defaults to None,
in which case observations for all the groups are computed and returned.
Returns:
Depending on the group's configuration, the tensors for individual observation terms are
concatenated along the last dimension into a single tensor. Otherwise, they are returned as
a dictionary with keys corresponding to the term's name.
Raises:
ValueError: If input ``group_name`` is not a valid group handled by the manager.
"""
# check ig group name is valid
if group_name not in self._group_obs_term_names:
raise ValueError(
f"Unable to find the group '{group_name}' in the observation manager."
f" Available groups are: {list(self._group_obs_term_names.keys())}"
)
# iterate over all the terms in each group
group_term_names = self._group_obs_term_names[group_name]
# buffer to store obs per group
group_obs = dict.fromkeys(group_term_names, None)
# read attributes for each term
obs_terms = zip(group_term_names, self._group_obs_term_cfgs[group_name])
# evaluate terms: compute, add noise, clip, scale, custom modifiers
for term_name, term_cfg in obs_terms:
# compute term's value
obs: torch.Tensor = term_cfg.func(self._env, **term_cfg.params).clone()
# apply post-processing
if term_cfg.modifiers is not None:
for modifier in term_cfg.modifiers:
obs = modifier.func(obs, **modifier.params)
if term_cfg.noise:
obs = term_cfg.noise.func(obs, term_cfg.noise)
if term_cfg.clip:
obs = obs.clip_(min=term_cfg.clip[0], max=term_cfg.clip[1])
if term_cfg.scale is not None:
obs = obs.mul_(term_cfg.scale)
# Update the history buffer if observation term has history enabled
if term_cfg.history_length > 0:
self._group_obs_term_history_buffer[group_name][term_name].append(obs)
if term_cfg.flatten_history_dim:
group_obs[term_name] = self._group_obs_term_history_buffer[group_name][term_name].buffer.reshape(
self._env.num_envs, -1
)
else:
group_obs[term_name] = self._group_obs_term_history_buffer[group_name][term_name].buffer
else:
group_obs[term_name] = obs
# concatenate all observations in the group together
if self._group_obs_concatenate[group_name]:
return torch.cat(list(group_obs.values()), dim=-1)
else:
return group_obs
"""
Helper functions.
"""
def _prepare_terms(self):
"""Prepares a list of observation terms functions."""
# create buffers to store information for each observation group
# TODO: Make this more convenient by using data structures.
self._group_obs_term_names: dict[str, list[str]] = dict()
self._group_obs_term_dim: dict[str, list[tuple[int, ...]]] = dict()
self._group_obs_term_cfgs: dict[str, list[ObservationTermCfg]] = dict()
self._group_obs_class_term_cfgs: dict[str, list[ObservationTermCfg]] = dict()
self._group_obs_concatenate: dict[str, bool] = dict()
self._group_obs_term_history_buffer: dict[str, dict] = dict()
# create a list to store modifiers that are classes
# we store it as a separate list to only call reset on them and prevent unnecessary calls
self._group_obs_class_modifiers: list[modifiers.ModifierBase] = list()
# check if config is dict already
if isinstance(self.cfg, dict):
group_cfg_items = self.cfg.items()
else:
group_cfg_items = self.cfg.__dict__.items()
# iterate over all the groups
for group_name, group_cfg in group_cfg_items:
# check for non config
if group_cfg is None:
continue
# check if the term is a curriculum term
if not isinstance(group_cfg, ObservationGroupCfg):
raise TypeError(
f"Observation group '{group_name}' is not of type 'ObservationGroupCfg'."
f" Received: '{type(group_cfg)}'."
)
# initialize list for the group settings
self._group_obs_term_names[group_name] = list()
self._group_obs_term_dim[group_name] = list()
self._group_obs_term_cfgs[group_name] = list()
self._group_obs_class_term_cfgs[group_name] = list()
group_entry_history_buffer: dict[str, CircularBuffer] = dict()
# read common config for the group
self._group_obs_concatenate[group_name] = group_cfg.concatenate_terms
# check if config is dict already
if isinstance(group_cfg, dict):
group_cfg_items = group_cfg.items()
else:
group_cfg_items = group_cfg.__dict__.items()
# iterate over all the terms in each group
for term_name, term_cfg in group_cfg_items:
# skip non-obs settings
if term_name in ["enable_corruption", "concatenate_terms", "history_length", "flatten_history_dim"]:
continue
# check for non config
if term_cfg is None:
continue
if not isinstance(term_cfg, ObservationTermCfg):
raise TypeError(
f"Configuration for the term '{term_name}' is not of type ObservationTermCfg."
f" Received: '{type(term_cfg)}'."
)
# resolve common terms in the config
self._resolve_common_term_cfg(f"{group_name}/{term_name}", term_cfg, min_argc=1)
# check noise settings
if not group_cfg.enable_corruption:
term_cfg.noise = None
# check group history params and override terms
if group_cfg.history_length is not None:
term_cfg.history_length = group_cfg.history_length
term_cfg.flatten_history_dim = group_cfg.flatten_history_dim
# add term config to list to list
self._group_obs_term_names[group_name].append(term_name)
self._group_obs_term_cfgs[group_name].append(term_cfg)
# call function the first time to fill up dimensions
obs_dims = tuple(term_cfg.func(self._env, **term_cfg.params).shape)
# create history buffers and calculate history term dimensions
if term_cfg.history_length > 0:
group_entry_history_buffer[term_name] = CircularBuffer(
max_len=term_cfg.history_length, batch_size=self._env.num_envs, device=self._env.device
)
old_dims = list(obs_dims)
old_dims.insert(1, term_cfg.history_length)
obs_dims = tuple(old_dims)
if term_cfg.flatten_history_dim:
obs_dims = (obs_dims[0], np.prod(obs_dims[1:]))
self._group_obs_term_dim[group_name].append(obs_dims[1:])
# if scale is set, check if single float or tuple
if term_cfg.scale is not None:
if not isinstance(term_cfg.scale, (float, int, tuple)):
raise TypeError(
f"Scale for observation term '{term_name}' in group '{group_name}'"
f" is not of type float, int or tuple. Received: '{type(term_cfg.scale)}'."
)
if isinstance(term_cfg.scale, tuple) and len(term_cfg.scale) != obs_dims[1]:
raise ValueError(
f"Scale for observation term '{term_name}' in group '{group_name}'"
f" does not match the dimensions of the observation. Expected: {obs_dims[1]}"
f" but received: {len(term_cfg.scale)}."
)
# cast the scale into torch tensor
term_cfg.scale = torch.tensor(term_cfg.scale, dtype=torch.float, device=self._env.device)
# prepare modifiers for each observation
if term_cfg.modifiers is not None:
# initialize list of modifiers for term
for mod_cfg in term_cfg.modifiers:
# check if class modifier and initialize with observation size when adding
if isinstance(mod_cfg, modifiers.ModifierCfg):
# to list of modifiers
if inspect.isclass(mod_cfg.func):
if not issubclass(mod_cfg.func, modifiers.ModifierBase):
raise TypeError(
f"Modifier function '{mod_cfg.func}' for observation term '{term_name}'"
f" is not a subclass of 'ModifierBase'. Received: '{type(mod_cfg.func)}'."
)
mod_cfg.func = mod_cfg.func(cfg=mod_cfg, data_dim=obs_dims, device=self._env.device)
# add to list of class modifiers
self._group_obs_class_modifiers.append(mod_cfg.func)
else:
raise TypeError(
f"Modifier configuration '{mod_cfg}' of observation term '{term_name}' is not of"
f" required type ModifierCfg, Received: '{type(mod_cfg)}'"
)
# check if function is callable
if not callable(mod_cfg.func):
raise AttributeError(
f"Modifier '{mod_cfg}' of observation term '{term_name}' is not callable."
f" Received: {mod_cfg.func}"
)
# check if term's arguments are matched by params
term_params = list(mod_cfg.params.keys())
args = inspect.signature(mod_cfg.func).parameters
args_with_defaults = [arg for arg in args if args[arg].default is not inspect.Parameter.empty]
args_without_defaults = [arg for arg in args if args[arg].default is inspect.Parameter.empty]
args = args_without_defaults + args_with_defaults
# ignore first two arguments for env and env_ids
# Think: Check for cases when kwargs are set inside the function?
if len(args) > 1:
if set(args[1:]) != set(term_params + args_with_defaults):
raise ValueError(
f"Modifier '{mod_cfg}' of observation term '{term_name}' expects"
f" mandatory parameters: {args_without_defaults[1:]}"
f" and optional parameters: {args_with_defaults}, but received: {term_params}."
)
# add term in a separate list if term is a class
if isinstance(term_cfg.func, ManagerTermBase):
self._group_obs_class_term_cfgs[group_name].append(term_cfg)
# call reset (in-case above call to get obs dims changed the state)
term_cfg.func.reset()
# add history buffers for each group
self._group_obs_term_history_buffer[group_name] = group_entry_history_buffer
