SAPIEN: A SimulAted Part-based Interactive ENvironment (2003.08515v1)

Abstract: Building home assistant robots has long been a pursuit for vision and robotics researchers. To achieve this task, a simulated environment with physically realistic simulation, sufficient articulated objects, and transferability to the real robot is indispensable. Existing environments achieve these requirements for robotics simulation with different levels of simplification and focus. We take one step further in constructing an environment that supports household tasks for training robot learning algorithm. Our work, SAPIEN, is a realistic and physics-rich simulated environment that hosts a large-scale set for articulated objects. Our SAPIEN enables various robotic vision and interaction tasks that require detailed part-level understanding.We evaluate state-of-the-art vision algorithms for part detection and motion attribute recognition as well as demonstrate robotic interaction tasks using heuristic approaches and reinforcement learning algorithms. We hope that our SAPIEN can open a lot of research directions yet to be explored, including learning cognition through interaction, part motion discovery, and construction of robotics-ready simulated game environment.

• The paper introduces a physically realistic simulation using the PhysX engine and ROS, enabling detailed interactions with articulated objects.
• It leverages the extensive PartNet-Mobility dataset of 2,346 objects and 14,068 parts across 46 categories, achieving state-of-the-art performance in perception tasks.
• The environment supports diverse robotic tasks including movable part detection and object manipulation, laying the groundwork for robust reinforcement learning strategies.

Overview of SAPIEN: A Simulated Part-Based Interactive Environment

The paper introduces SAPIEN, a comprehensive simulation environment designed to advance research in robotics and computer vision, with a particular focus on the exploration and manipulation of articulated objects. Unlike previous simulation platforms, SAPIEN is characterized by its sophisticated physical realism, which is achieved through the integration of PhysX physics engine along with ROS for robot control interfaces. The environment is enriched with an extensive dataset, the PartNet-Mobility dataset, that encompasses thousands of 3D models annotated with mobility information, making it particularly suited for tasks requiring intricate object interaction.

Primary Features of SAPIEN

1. Physically Realistic Simulation: SAPIEN utilizes the PhysX physics engine to provide detailed simulations of rigid bodies and joints integral to the realistic representation of articulated objects and robot interactions.
2. PartNet-Mobility Dataset: The environment features a vast collection of 2,346 objects with 14,068 movable parts classified into 46 categories, distinguished by varied textures and motion annotations that make them simulation-ready.
3. Rendering and Robotics Interface: Two core design pillars in SAPIEN, the rendering engine, with both OpenGL and ray-tracing capabilities, supports realistic visual feedback, while the inclusion of the Robot Operating System (ROS) interface facilitates seamless robotics research and deployment.
4. Support for Diverse Robotic Tasks: SAPIEN is tailored to accommodate a wide range of robotic perception tasks—movable part detection, part motion recognition—and interaction tasks such as door opening and drawer manipulation, either through predetermined heuristics or reinforcement learning methods.

Experimental Evaluations and Observations

In validating their environment's capabilities, the authors demonstrate compelling benchmarks on perception and interaction tasks. For part detection, state-of-the-art results were produced using well-established frameworks like Mask R-CNN and PartNet-InsSeg on RGB and point cloud data, respectively. Each framework is evaluated on accuracy metrics such as average precision, with notable findings on the effect of part size on detection efficacy.

For robotic interaction, SAPIEN enables manipulation tasks using both heuristic methods and reinforcement learning, designed to capitalize on the environment's large dataset and the accurate simulation of dynamic object behavior. Here, tasks are evaluated based on success rates and generalization capabilities across unseen objects—critical aspects for realistic robot training.

Implications and Future Directions

The innovations in SAPIEN offer significant implications for both theoretical and applied robotics research. The physics-rich nature of the simulation allows for high-fidelity benchmarks of algorithms, potentially leading to developments in robust robot learning strategies and more effective symbiosis between perception and action in computational systems. The comprehensive dataset opens the door for investigating learning paradigms that can generalize across object categories and handle novel situations without requiring exhaustive retraining.

Future research directions, as suggested, may revolve around enhancing the visual processing capabilities to better encode geometric information, improving the generalization of reinforcement learning agents on complex tasks, and leveraging the environment for broader cognitive learning paradigms in AI systems. By offering a robust foundational paradigm, SAPIEN aims to catalyze advances in home-assistant robotics and beyond, achieving greater alignment between simulation and real-world transferability.