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Merge branch 'main' of github.com:isaac-sim/IsaacLab into camera_limi…
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kellyguo11 committed Oct 23, 2024
2 parents b5fe0ec + 6bc4d0a commit 102616e
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2 changes: 2 additions & 0 deletions CONTRIBUTORS.md
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Expand Up @@ -45,6 +45,7 @@ Guidelines for modifications:
* Gary Lvov
* Giulio Romualdi
* HoJin Jeon
* Jan Kerner
* Jean Tampon
* Jia Lin Yuan
* Jingzhou Liu
Expand All @@ -70,6 +71,7 @@ Guidelines for modifications:
* Zhengyu Zhang
* Ziqi Fan
* Qian Wan
* Wei Yang

## Acknowledgements

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1 change: 0 additions & 1 deletion source/extensions/omni.isaac.lab/config/extension.toml
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Expand Up @@ -13,7 +13,6 @@ keywords = ["kit", "robotics", "learning", "ai"]

[dependencies]
"omni.isaac.core" = {}
"omni.isaac.ml_archive" = {}
"omni.replicator.core" = {}

[python.pipapi]
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Expand Up @@ -185,7 +185,7 @@ def reset(self, env_ids: Sequence[int] | None = None) -> None:
env_ids = slice(None)
super().reset(env_ids)
# reset history to current joint positions
self._prev_applied_actions[env_ids, :] = self._asset.data.joint_pos[env_ids, self._joint_ids]
self._prev_applied_actions[env_ids, :] = self._asset.data.joint_pos[env_ids[:, None], self._joint_ids]

def process_actions(self, actions: torch.Tensor):
# apply affine transformations
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@@ -1,7 +1,7 @@
[package]

# Note: Semantic Versioning is used: https://semver.org/
version = "0.10.7"
version = "0.10.8"

# Description
title = "Isaac Lab Environments"
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10 changes: 10 additions & 0 deletions source/extensions/omni.isaac.lab_tasks/docs/CHANGELOG.rst
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@@ -1,6 +1,16 @@
Changelog
---------

0.10.8 (2024-10-22)
~~~~~~~~~~~~~~~~~~~

Fixed
^^^^^

* Fixed the wrong selection of body id's in the :meth:`omni.isaac.lab_tasks.manager_based.locomotion.velocity.mdp.rewards.feet_slide`
reward function. This makes sure the right IDs are selected for the bodies.


0.10.7 (2024-10-02)
~~~~~~~~~~~~~~~~~~~

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Expand Up @@ -77,7 +77,7 @@ def feet_slide(env, sensor_cfg: SceneEntityCfg, asset_cfg: SceneEntityCfg = Scen
contact_sensor: ContactSensor = env.scene.sensors[sensor_cfg.name]
contacts = contact_sensor.data.net_forces_w_history[:, :, sensor_cfg.body_ids, :].norm(dim=-1).max(dim=1)[0] > 1.0
asset = env.scene[asset_cfg.name]
body_vel = asset.data.body_lin_vel_w[:, asset_cfg.body_ids, :2]
body_vel = asset.data.body_lin_vel_w[:, sensor_cfg.body_ids, :2]
reward = torch.sum(body_vel.norm(dim=-1) * contacts, dim=1)
return reward

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