sapien 1.1.0

['SAPIEN: A SimulAted Parted based Interactive ENvironment']

SAPIEN: A SimulAted Part-based Interactive ENvironment

SAPIEN is a realistic and physics-rich simulated environment that hosts a large-scale set for articulated objects. It enables various robotic vision and interaction tasks that require detailed part-level understanding. SAPIEN is a collaborative effort between researchers at UCSD, Stanford and SFU. The dataset is a continuation of ShapeNet and PartNet.

Change Log

1.2

• Shader change: 4th component in default camera shader now gives the 0-1 depth value.
• Add "critical" and "off" log levels.
• Add support for pointcloud and line rendering (for visualizing camera and point cloud)
• Performance: the same shader only compile once per process
• Bug fix
  • Articulation setDriveTarget was now correctly reversed for prismatic joint (joint setDriveTarget is not affected)
  • Fix kinematic articulation loader

1.1

• Support nonconvex static/kinematic collision shape
• Add warning for small mass/inertia
• Introduce Entity as the base class of Actors
• Add Light classes inherited from entity, allowing manipulate light objects in sapien scene
• Updates to the viewer
  • rename actor to entity when appropriate
• Partial support the material tag in URDF loader (primitive shape, single color)
• Bug fixes for the renderer
• Support inner and outer FOV for spotlight

1.0

• Replace the old Vulkan based renderer completely
  • See sapien.core.renderer for details
• Expose GUI functionalities to Python
• Reimplement Vulkan viewer in Python
• Expose PhysX shape wrapper to Python. For example,
  • Collision shapes can be retrieved through actor.get_collision_shapes
  • Collision groups on a shape can be set by CollisionShape.set_collision_groups
  • Shapes are now also available in Contact.
• API changes
  • Render material creation is now renderer.create_material()
  • in actor builder: add_xxx_shape is replaced with add_xxx_collision.
  • move light functions from scene to scene.renderer_scene
• Add centrifugal and Coriolis force.
• Change default physical parameters for better stability.

SAPIEN Engine

SAPIEN Engine provides physical simulation for articulated objects. It powers reinforcement learning and robotics with its pure Python interface.

SAPIEN Renderer

SAPIEN provides rasterized and ray traced (available soon) rendering with Vulkan. For machines without Vulkan support, we provide a OpenGL renderer as a second option.

PartNet-Mobility

SAPIEN releases PartNet-Mobility dataset, which is a collection of 2K articulated objects with motion annotations and rendernig material. The dataset powers research for generalizable computer vision and manipulation.

Website and Documentation

SAPIEN Website: https://sapien.ucsd.edu/. SAPIEN Documentation: https://sapien.ucsd.edu/docs/index.html.

Build from source

Before build

Make sure all submodules are initialized git submodule update --init --recursive.

Build with Docker

To build SAPIEN, simply run ./docker_build_wheels.sh. It is not recommended to build outside of our provided docker.

For reference, the Dockerfile is provided here. Note that PhysX needs to be compiled with clang-9 into static libraries before building the Docker image.

Build without Docker

It can be tricky to setup all dependencies outside of a Docker environment. You need to install all dependencies according to the Docker environment. If all dependencies set up correctly, run python setup.py bdist_wheel to build the wheel.

Cite SAPIEN

If you use SAPIEN and its assets, please cite the following works.

@InProceedings{Xiang_2020_SAPIEN,
author = {Xiang, Fanbo and Qin, Yuzhe and Mo, Kaichun and Xia, Yikuan and Zhu, Hao and Liu, Fangchen and Liu, Minghua and Jiang, Hanxiao and Yuan, Yifu and Wang, He and Yi, Li and Chang, Angel X. and Guibas, Leonidas J. and Su, Hao},
title = {{SAPIEN}: A SimulAted Part-based Interactive ENvironment},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}}

@InProceedings{Mo_2019_CVPR,
author = {Mo, Kaichun and Zhu, Shilin and Chang, Angel X. and Yi, Li and Tripathi, Subarna and Guibas, Leonidas J. and Su, Hao},
title = {{PartNet}: A Large-Scale Benchmark for Fine-Grained and Hierarchical Part-Level {3D} Object Understanding},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}

@article{chang2015shapenet,
title={Shapenet: An information-rich 3d model repository},
author={Chang, Angel X and Funkhouser, Thomas and Guibas, Leonidas and Hanrahan, Pat and Huang, Qixing and Li, Zimo and Savarese, Silvio and Savva, Manolis and Song, Shuran and Su, Hao and others},
journal={arXiv preprint arXiv:1512.03012},
year={2015}
}