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StoneT2000 committed Jan 12, 2024
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2 changes: 1 addition & 1 deletion README.md
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Expand Up @@ -31,7 +31,7 @@ From pip:

```bash
# NOTE: This is a beta build at the moment. To install the beta build run
pip install mani-skill2==0.6.0.dev2
pip install mani-skill2==0.6.0.dev3
```

From github:
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2 changes: 1 addition & 1 deletion docs/source/concepts/observation.md
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Expand Up @@ -96,7 +96,7 @@ For `obs_mode="pointcloud"`:
An "actor" is a fundamental object that represents a physical entity (rigid body) that can be simulated in SAPIEN (the backend of ManiSkill2). An articulated object is a collection of links interconnected by joints, and each link is also an actor. In SAPIEN, `scene.get_all_actors()` will return all the actors that are not links of articulated objects. The examples are the ground, the cube in [PickCube](./environments.md#pickcube-v0), and the YCB objects in [PickSingleYCB](./environments.md#picksingleycb-v0). `scene.get_all_articulations()` will return all the articulations. The examples are the robots, the cabinets in [OpenCabinetDoor](./environments.md#opencabinetdoor-v1), and the chairs in [PushChair](./environments.md#pushchair-v1). Below is an example of how to get actors and articulations in SAPIEN.

```python
import sapien.core as sapien
import sapien

scene: sapien.Scene = ...
actors = scene.get_all_actors() # actors excluding links
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22 changes: 0 additions & 22 deletions examples/profile_speed/README.md

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34 changes: 0 additions & 34 deletions examples/profile_speed/profile_dmcontrol.py

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270 changes: 0 additions & 270 deletions examples/profile_speed/profile_sapien.py

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8 changes: 0 additions & 8 deletions examples/submission/Dockerfile

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14 changes: 0 additions & 14 deletions examples/submission/user_solution.py

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3 changes: 1 addition & 2 deletions mani_skill2/envs/misc/avoid_obstacles.py
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Expand Up @@ -8,7 +8,6 @@

from mani_skill2 import format_path
from mani_skill2.agents.robots.panda import PandaRealSensed435
from mani_skill2.agents.robots.panda.panda import Panda
from mani_skill2.envs.sapien_env import BaseEnv
from mani_skill2.sensors.camera import CameraConfig
from mani_skill2.utils.building.ground import build_tesselated_square_floor
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def evaluate(self, **kwargs) -> dict:
tcp_pose_at_goal = self.goal_pose.inv() * self.tcp.pose
pos_dist = np.linalg.norm(tcp_pose_at_goal.p)
ang_dist = np.arccos(tcp_pose_at_goal.q[0]) * 2
ang_dist = np.arccos(np.clip(tcp_pose_at_goal.q[0], -1, 1)) * 2
if ang_dist > np.pi: # [0, 2 * pi] -> [-pi, pi]
ang_dist = ang_dist - 2 * np.pi
ang_dist = np.abs(ang_dist)
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