AnyGrasp: Robust and Efficient Grasp Perception in Spatial and Temporal Domains (2212.08333v2)

Abstract: As the basis for prehensile manipulation, it is vital to enable robots to grasp as robustly as humans. Our innate grasping system is prompt, accurate, flexible, and continuous across spatial and temporal domains. Few existing methods cover all these properties for robot grasping. In this paper, we propose AnyGrasp for grasp perception to enable robots these abilities using a parallel gripper. Specifically, we develop a dense supervision strategy with real perception and analytic labels in the spatial-temporal domain. Additional awareness of objects' center-of-mass is incorporated into the learning process to help improve grasping stability. Utilization of grasp correspondence across observations enables dynamic grasp tracking. Our model can efficiently generate accurate, 7-DoF, dense, and temporally-smooth grasp poses and works robustly against large depth-sensing noise. Using AnyGrasp, we achieve a 93.3% success rate when clearing bins with over 300 unseen objects, which is on par with human subjects under controlled conditions. Over 900 mean-picks-per-hour is reported on a single-arm system. For dynamic grasping, we demonstrate catching swimming robot fish in the water. Our project page is at https://graspnet.net/anygrasp.html

• The paper presents AnyGrasp, a novel system for robust and efficient robotic grasp perception that operates across spatial and temporal domains, integrating dense supervision and object center-of-mass awareness to improve stability against depth noise.
• AnyGrasp demonstrates high performance, achieving a 93.3% grasp success rate on over 300 unknown objects and reaching over 900 mean picks per hour in cluttered environments, matching human performance under controlled conditions.
• The system enables dynamic grasping of moving targets and exhibits strong resilience to sensor noise by training on real-world data, bridging the gap between human and robotic grasp perception for practical applications.

Overview of "AnyGrasp: Robust and Efficient Grasp Perception in Spatial and Temporal Domains"

The paper presents "AnyGrasp," a novel system designed to significantly enhance robotic grasping capabilities. The system seeks to emulate the proficiency of human grasping by effectively operating across both spatial and temporal domains, utilizing a parallel gripper. It addresses limitations of existing methods by integrating a dense supervision strategy and incorporating object center-of-mass awareness, which aids in improving grasp stability and robustness against large depth-sensing noise.

The authors propose a methodology that enables robots to generate accurate, dense, and temporally smooth grasp poses. Specifically, AnyGrasp achieves this by leveraging grasp correspondence across multiple observations, thereby facilitating dynamic grasp tracking. The approach allows the robot to grasp in complex environments with a reported success rate of 93.3% in cluttered scenes containing over 300 unknown objects, outperforming many existing systems and matching human performance under controlled conditions. Remarkably, the system also achieves over 900 mean picks per hour.

Key Contributions

1. Unified Grasp Detection System: AnyGrasp is the first system to synergize spatially continuous learning with temporally continuous learning. By processing a complete scene, it estimates dense 7-degree-of-freedom (7-DoF) grasp configurations, essential for dynamic situations.
2. Incorporating Center-of-Mass Awareness: The system integrates knowledge about the object's center of mass into its learning process, enhancing grasp stability—a feature often neglected in prior research.
3. Real-World Data and Dense Supervision Strategy: Unlike many competing approaches relying primarily on simulated data, AnyGrasp leverages a real-world dataset, demonstrating superior performance. This real-world grounding in data collection allows the system to better handle sensor noise common in commercial depth cameras.
4. Library Release: The authors release a comprehensive grasping library showcasing the AnyGrasp system's capabilities, facilitating reproducibility and further research in this area.

Experimental Outcomes

The paper outlines extensive experimental validations across diverse scenarios:

• Generalization across Diverse Object Sets: The system was tested with over 300 previously unseen objects, achieving a 93.3% success rate, comparable to human subjects utilizing the same gripper configuration.
• Dynamic Grasping Capabilities: AnyGrasp showcased its ability to apprehend moving targets, such as robot fish in an aquarium, underscoring its effectiveness in dynamic environments—a challenge where existing static grasper designs fall short.
• Handling of Sensor Noise: By training on real-world data, AnyGrasp shows notable resilience against variations in sensory data, particularly with commonly used depth cameras like Intel RealSense.

Implications and Future Directions

The research has significant implications for advancing the practical deployment of robotic systems in both industrial and domestic settings, paving the way for more adaptable and intelligent robots. By bridging the grasp perception gap between humans and robots, AnyGrasp demonstrates the potential for robots to perform complex manipulation tasks with higher autonomy and efficiency.

Future work could extend the principles established by AnyGrasp to more dexterous gripper designs and explore the integration of multisensory feedback, such as force or tactile sensing, to further emulate the sensory-rich grasping capabilities of humans. Further exploration of self-supervised learning approaches using this system can refine robotic adaptability in unmodeled environments, enhancing applications across diverse sectors.