Merge remote-tracking branch 'upstream/main' into main

.pre-commit-config.yaml

@@ -38,7 +38,7 @@ repos:
       - id: pyupgrade
         args: ["--py310-plus"]
         # FIXME: This is a hack because Pytorch does not like: torch.Tensor | dict aliasing
-        exclude: "source/extensions/omni.isaac.lab/omni/isaac/lab/envs/common.py"
+        exclude: "source/extensions/omni.isaac.lab/omni/isaac/lab/envs/common.py|source/extensions/omni.isaac.lab/omni/isaac/lab/ui/widgets/image_plot.py"
   - repo: https://github.com/codespell-project/codespell
     rev: v2.2.6
     hooks:

source/extensions/omni.isaac.lab/config/extension.toml

@@ -1,7 +1,7 @@
 [package]
 
 # Note: Semantic Versioning is used: https://semver.org/
-version = "0.27.26"
+version = "0.29.1"
 
 # Description
 title = "Isaac Lab framework for Robot Learning"

source/extensions/omni.isaac.lab/docs/CHANGELOG.rst

@@ -1,6 +1,68 @@
 Changelog
 ---------
 
+0.29.1 (2024-12-15)
+~~~~~~~~~~~~~~~~~~~
+
+Changed
+^^^^^^^
+
+* Added call to update articulation kinematics after reset to ensure states are updated for non-rendering sensors. Previously, some changes in reset such as modifying joint states would not be reflected in the rigid body states immediately after reset.
+
+
+0.29.0 (2024-12-15)
+~~~~~~~~~~~~~~~~~~~
+
+Added
+^^^^^
+
+* Added UI interface to the Managers in the ManagerBasedEnv and MangerBasedRLEnv classes.
+* Added UI widgets for :class:`LiveLinePlot` and :class:`ImagePlot`.
+* Added ``ManagerLiveVisualizer/Cfg``: Given a ManagerBase (i.e. action_manager, observation_manager, etc) and a config file this class creates the the interface between managers and the UI.
+* Added :class:`EnvLiveVisualizer`: A 'manager' of ManagerLiveVisualizer. This is added to the ManagerBasedEnv but is only called during the initialization of the managers in load_managers
+* Added ``get_active_iterable_terms`` implementation methods to ActionManager, ObservationManager, CommandsManager, CurriculumManager, RewardManager, and TerminationManager. This method exports the active term data and labels for each manager and is called by ManagerLiveVisualizer.
+* Additions to :class:`BaseEnvWindow` and :class:`RLEnvWindow` to register ManagerLiveVisualizer UI interfaces for the chosen managers.
+
+
+0.28.0 (2024-12-15)
+~~~~~~~~~~~~~~~~~~~
+
+Added
+^^^^^
+
+* Added observation history computation to :class:`omni.isaac.lab.manager.observation_manager.ObservationManager`.
+* Added ``history_length`` and ``flatten_history_dim`` configuration parameters to :class:`omni.isaac.lab.manager.manager_term_cfg.ObservationTermCfg`
+* Added ``history_length`` and ``flatten_history_dim`` configuration parameters to :class:`omni.isaac.lab.manager.manager_term_cfg.ObservationGroupCfg`
+* Added full buffer property to :class:`omni.isaac.lab.utils.buffers.circular_buffer.CircularBuffer`
+
+
+0.27.29 (2024-12-15)
+~~~~~~~~~~~~~~~~~~~~
+
+Added
+^^^^^
+
+* Added action clip to all :class:`omni.isaac.lab.envs.mdp.actions`.
+
+
+0.27.28 (2024-12-14)
+~~~~~~~~~~~~~~~~~~~~
+
+Changed
+^^^^^^^
+
+* Added check for error below threshold in state machines to ensure the state has been reached.
+
+
+0.27.27 (2024-12-13)
+~~~~~~~~~~~~~~~~~~~~
+
+Fixed
+^^^^^
+
+* Fixed the shape of ``quat_w`` in the ``apply_actions`` method of :attr:`~omni.isaac.lab.env.mdp.NonHolonomicAction` (previously (N,B,4), now (N,4) since the number of root bodies B is required to be 1). Previously ``apply_actions`` errored because ``euler_xyz_from_quat`` requires inputs of shape (N,4).
+
+
 0.27.26 (2024-12-11)
 ~~~~~~~~~~~~~~~~~~~~
 
@@ -54,7 +116,7 @@ Changed
 
 
 0.27.21 (2024-12-06)
-~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~
 
 Fixed
 ^^^^^

source/extensions/omni.isaac.lab/omni/isaac/lab/envs/direct_rl_env.py

@@ -273,6 +273,10 @@ def reset(self, seed: int | None = None, options: dict[str, Any] | None = None)
         indices = torch.arange(self.num_envs, dtype=torch.int64, device=self.device)
         self._reset_idx(indices)
 
+        # update articulation kinematics
+        self.scene.write_data_to_sim()
+        self.sim.forward()
+
         # 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()
@@ -346,6 +350,9 @@ def step(self, action: torch.Tensor) -> VecEnvStepReturn:
         reset_env_ids = self.reset_buf.nonzero(as_tuple=False).squeeze(-1)
         if len(reset_env_ids) > 0:
             self._reset_idx(reset_env_ids)
+            # update articulation kinematics
+            self.scene.write_data_to_sim()
+            self.sim.forward()
             # 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()

source/extensions/omni.isaac.lab/omni/isaac/lab/envs/manager_based_env.py

@@ -14,6 +14,7 @@
 from omni.isaac.lab.managers import ActionManager, EventManager, ObservationManager, RecorderManager
 from omni.isaac.lab.scene import InteractiveScene
 from omni.isaac.lab.sim import SimulationContext
+from omni.isaac.lab.ui.widgets import ManagerLiveVisualizer
 from omni.isaac.lab.utils.timer import Timer
 
 from .common import VecEnvObs
@@ -148,6 +149,8 @@ def __init__(self, cfg: ManagerBasedEnvCfg):
         # 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:
+            # setup live visualizers
+            self.setup_manager_visualizers()
             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
@@ -233,6 +236,14 @@ def load_managers(self):
         if self.__class__ == ManagerBasedEnv and "startup" in self.event_manager.available_modes:
             self.event_manager.apply(mode="startup")
 
+    def setup_manager_visualizers(self):
+        """Creates live visualizers for manager terms."""
+
+        self.manager_visualizers = {
+            "action_manager": ManagerLiveVisualizer(manager=self.action_manager),
+            "observation_manager": ManagerLiveVisualizer(manager=self.observation_manager),
+        }
+
     """
     Operations - MDP.
     """
@@ -269,15 +280,17 @@ def reset(
 
         # reset state of scene
         self._reset_idx(env_ids)
-        self.scene.write_data_to_sim()
-
-        # trigger recorder terms for post-reset calls
-        self.recorder_manager.record_post_reset(env_ids)
 
+        # update articulation kinematics
+        self.scene.write_data_to_sim()
+        self.sim.forward()
         # 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()
 
+        # trigger recorder terms for post-reset calls
+        self.recorder_manager.record_post_reset(env_ids)
+
         # compute observations
         self.obs_buf = self.observation_manager.compute()
 
@@ -317,13 +330,16 @@ def reset_to(
         # set the state
         self.scene.reset_to(state, env_ids, is_relative=is_relative)
 
-        # trigger recorder terms for post-reset calls
-        self.recorder_manager.record_post_reset(env_ids)
+        # update articulation kinematics
+        self.sim.forward()
 
         # 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()
 
+        # trigger recorder terms for post-reset calls
+        self.recorder_manager.record_post_reset(env_ids)
+
         # compute observations
         self.obs_buf = self.observation_manager.compute()
 

source/extensions/omni.isaac.lab/omni/isaac/lab/envs/manager_based_rl_env.py

@@ -16,6 +16,7 @@
 from omni.isaac.version import get_version
 
 from omni.isaac.lab.managers import CommandManager, CurriculumManager, RewardManager, TerminationManager
+from omni.isaac.lab.ui.widgets import ManagerLiveVisualizer
 
 from .common import VecEnvStepReturn
 from .manager_based_env import ManagerBasedEnv
@@ -132,6 +133,18 @@ def load_managers(self):
         if "startup" in self.event_manager.available_modes:
             self.event_manager.apply(mode="startup")
 
+    def setup_manager_visualizers(self):
+        """Creates live visualizers for manager terms."""
+
+        self.manager_visualizers = {
+            "action_manager": ManagerLiveVisualizer(manager=self.action_manager),
+            "observation_manager": ManagerLiveVisualizer(manager=self.observation_manager),
+            "command_manager": ManagerLiveVisualizer(manager=self.command_manager),
+            "termination_manager": ManagerLiveVisualizer(manager=self.termination_manager),
+            "reward_manager": ManagerLiveVisualizer(manager=self.reward_manager),
+            "curriculum_manager": ManagerLiveVisualizer(manager=self.curriculum_manager),
+        }
+
     """
     Operations - MDP
     """
@@ -204,9 +217,9 @@ def step(self, action: torch.Tensor) -> VecEnvStepReturn:
             self.recorder_manager.record_pre_reset(reset_env_ids)
 
             self._reset_idx(reset_env_ids)
-
-            # this is needed to make joint positions set from reset events effective
+            # update articulation kinematics
             self.scene.write_data_to_sim()
+            self.sim.forward()
 
             # 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:

source/extensions/omni.isaac.lab/omni/isaac/lab/envs/mdp/actions/binary_joint_actions.py

@@ -40,9 +40,10 @@ class BinaryJointAction(ActionTerm):
 
     cfg: actions_cfg.BinaryJointActionCfg
     """The configuration of the action term."""
-
     _asset: Articulation
     """The articulation asset on which the action term is applied."""
+    _clip: torch.Tensor
+    """The clip applied to the input action."""
 
     def __init__(self, cfg: actions_cfg.BinaryJointActionCfg, env: ManagerBasedEnv) -> None:
         # initialize the action term
@@ -83,6 +84,17 @@ def __init__(self, cfg: actions_cfg.BinaryJointActionCfg, env: ManagerBasedEnv)
             )
         self._close_command[index_list] = torch.tensor(value_list, device=self.device)
 
+        # parse clip
+        if self.cfg.clip is not None:
+            if isinstance(cfg.clip, dict):
+                self._clip = torch.tensor([[-float("inf"), float("inf")]], device=self.device).repeat(
+                    self.num_envs, self.action_dim, 1
+                )
+                index_list, _, value_list = string_utils.resolve_matching_names_values(self.cfg.clip, self._joint_names)
+                self._clip[:, index_list] = torch.tensor(value_list, device=self.device)
+            else:
+                raise ValueError(f"Unsupported clip type: {type(cfg.clip)}. Supported types are dict.")
+
     """
     Properties.
     """
@@ -115,6 +127,10 @@ def process_actions(self, actions: torch.Tensor):
             binary_mask = actions < 0
         # compute the command
         self._processed_actions = torch.where(binary_mask, self._close_command, self._open_command)
+        if self.cfg.clip is not None:
+            self._processed_actions = torch.clamp(
+                self._processed_actions, min=self._clip[:, :, 0], max=self._clip[:, :, 1]
+            )
 
     def reset(self, env_ids: Sequence[int] | None = None) -> None:
         self._raw_actions[env_ids] = 0.0

source/extensions/omni.isaac.lab/omni/isaac/lab/envs/mdp/actions/joint_actions.py

@@ -50,6 +50,8 @@ class JointAction(ActionTerm):
     """The scaling factor applied to the input action."""
     _offset: torch.Tensor | float
     """The offset applied to the input action."""
+    _clip: torch.Tensor
+    """The clip applied to the input action."""
 
     def __init__(self, cfg: actions_cfg.JointActionCfg, env: ManagerBasedEnv) -> None:
         # initialize the action term
@@ -94,6 +96,16 @@ def __init__(self, cfg: actions_cfg.JointActionCfg, env: ManagerBasedEnv) -> Non
             self._offset[:, index_list] = torch.tensor(value_list, device=self.device)
         else:
             raise ValueError(f"Unsupported offset type: {type(cfg.offset)}. Supported types are float and dict.")
+        # parse clip
+        if self.cfg.clip is not None:
+            if isinstance(cfg.clip, dict):
+                self._clip = torch.tensor([[-float("inf"), float("inf")]], device=self.device).repeat(
+                    self.num_envs, self.action_dim, 1
+                )
+                index_list, _, value_list = string_utils.resolve_matching_names_values(self.cfg.clip, self._joint_names)
+                self._clip[:, index_list] = torch.tensor(value_list, device=self.device)
+            else:
+                raise ValueError(f"Unsupported clip type: {type(cfg.clip)}. Supported types are dict.")
 
     """
     Properties.
@@ -120,6 +132,11 @@ def process_actions(self, actions: torch.Tensor):
         self._raw_actions[:] = actions
         # apply the affine transformations
         self._processed_actions = self._raw_actions * self._scale + self._offset
+        # clip actions
+        if self.cfg.clip is not None:
+            self._processed_actions = torch.clamp(
+                self._processed_actions, min=self._clip[:, :, 0], max=self._clip[:, :, 1]
+            )
 
     def reset(self, env_ids: Sequence[int] | None = None) -> None:
         self._raw_actions[env_ids] = 0.0

source/extensions/omni.isaac.lab/omni/isaac/lab/envs/mdp/actions/joint_actions_to_limits.py

@@ -44,6 +44,8 @@ class JointPositionToLimitsAction(ActionTerm):
     """The articulation asset on which the action term is applied."""
     _scale: torch.Tensor | float
     """The scaling factor applied to the input action."""
+    _clip: torch.Tensor
+    """The clip applied to the input action."""
 
     def __init__(self, cfg: actions_cfg.JointPositionToLimitsActionCfg, env: ManagerBasedEnv):
         # initialize the action term
@@ -76,6 +78,16 @@ def __init__(self, cfg: actions_cfg.JointPositionToLimitsActionCfg, env: Manager
             self._scale[:, index_list] = torch.tensor(value_list, device=self.device)
         else:
             raise ValueError(f"Unsupported scale type: {type(cfg.scale)}. Supported types are float and dict.")
+        # parse clip
+        if self.cfg.clip is not None:
+            if isinstance(cfg.clip, dict):
+                self._clip = torch.tensor([[-float("inf"), float("inf")]], device=self.device).repeat(
+                    self.num_envs, self.action_dim, 1
+                )
+                index_list, _, value_list = string_utils.resolve_matching_names_values(self.cfg.clip, self._joint_names)
+                self._clip[:, index_list] = torch.tensor(value_list, device=self.device)
+            else:
+                raise ValueError(f"Unsupported clip type: {type(cfg.clip)}. Supported types are dict.")
 
     """
     Properties.
@@ -102,6 +114,10 @@ def process_actions(self, actions: torch.Tensor):
         self._raw_actions[:] = actions
         # apply affine transformations
         self._processed_actions = self._raw_actions * self._scale
+        if self.cfg.clip is not None:
+            self._processed_actions = torch.clamp(
+                self._processed_actions, min=self._clip[:, :, 0], max=self._clip[:, :, 1]
+            )
         # rescale the position targets if configured
         # this is useful when the input actions are in the range [-1, 1]
         if self.cfg.rescale_to_limits:

source/extensions/omni.isaac.lab/omni/isaac/lab/envs/mdp/actions/non_holonomic_actions.py

@@ -11,6 +11,7 @@
 
 import omni.log
 
+import omni.isaac.lab.utils.string as string_utils
 from omni.isaac.lab.assets.articulation import Articulation
 from omni.isaac.lab.managers.action_manager import ActionTerm
 from omni.isaac.lab.utils.math import euler_xyz_from_quat
@@ -59,6 +60,8 @@ class NonHolonomicAction(ActionTerm):
     """The scaling factor applied to the input action. Shape is (1, 2)."""
     _offset: torch.Tensor
     """The offset applied to the input action. Shape is (1, 2)."""
+    _clip: torch.Tensor
+    """The clip applied to the input action."""
 
     def __init__(self, cfg: actions_cfg.NonHolonomicActionCfg, env: ManagerBasedEnv):
         # initialize the action term
@@ -104,6 +107,16 @@ def __init__(self, cfg: actions_cfg.NonHolonomicActionCfg, env: ManagerBasedEnv)
         # save the scale and offset as tensors
         self._scale = torch.tensor(self.cfg.scale, device=self.device).unsqueeze(0)
         self._offset = torch.tensor(self.cfg.offset, device=self.device).unsqueeze(0)
+        # parse clip
+        if self.cfg.clip is not None:
+            if isinstance(cfg.clip, dict):
+                self._clip = torch.tensor([[-float("inf"), float("inf")]], device=self.device).repeat(
+                    self.num_envs, self.action_dim, 1
+                )
+                index_list, _, value_list = string_utils.resolve_matching_names_values(self.cfg.clip, self._joint_names)
+                self._clip[:, index_list] = torch.tensor(value_list, device=self.device)
+            else:
+                raise ValueError(f"Unsupported clip type: {type(cfg.clip)}. Supported types are dict.")
 
     """
     Properties.
@@ -129,10 +142,15 @@ def process_actions(self, actions):
         # store the raw actions
         self._raw_actions[:] = actions
         self._processed_actions = self.raw_actions * self._scale + self._offset
+        # clip actions
+        if self.cfg.clip is not None:
+            self._processed_actions = torch.clamp(
+                self._processed_actions, min=self._clip[:, :, 0], max=self._clip[:, :, 1]
+            )
 
     def apply_actions(self):
         # obtain current heading
-        quat_w = self._asset.data.body_quat_w[:, self._body_idx]
+        quat_w = self._asset.data.body_quat_w[:, self._body_idx].view(self.num_envs, 4)
         yaw_w = euler_xyz_from_quat(quat_w)[2]
         # compute joint velocities targets
         self._joint_vel_command[:, 0] = torch.cos(yaw_w) * self.processed_actions[:, 0]  # x