RGBGrasp: Image-based Object Grasping by Capturing Multiple Views during Robot Arm Movement with Neural Radiance Fields (2311.16592v2)

Abstract: Robotic research encounters a significant hurdle when it comes to the intricate task of grasping objects that come in various shapes, materials, and textures. Unlike many prior investigations that heavily leaned on specialized point-cloud cameras or abundant RGB visual data to gather 3D insights for object-grasping missions, this paper introduces a pioneering approach called RGBGrasp. This method depends on a limited set of RGB views to perceive the 3D surroundings containing transparent and specular objects and achieve accurate grasping. Our method utilizes pre-trained depth prediction models to establish geometry constraints, enabling precise 3D structure estimation, even under limited view conditions. Finally, we integrate hash encoding and a proposal sampler strategy to significantly accelerate the 3D reconstruction process. These innovations significantly enhance the adaptability and effectiveness of our algorithm in real-world scenarios. Through comprehensive experimental validations, we demonstrate that RGBGrasp achieves remarkable success across a wide spectrum of object-grasping scenarios, establishing it as a promising solution for real-world robotic manipulation tasks. The demonstrations of our method can be found on: https://sites.google.com/view/rgbgrasp

• The paper presents an innovative RGB-based algorithm that integrates monocular depth estimation with dynamic view capture for precise 3D object grasping.
• It combines a hash encoding strategy with a proposal sampler to accelerate NeRF reconstruction, maintaining over 80% success rate in varied material conditions.
• Experimental results in simulation and physical tests demonstrate reduced depth RMSE and robust performance in cluttered, constrained environments.

Overview of RGBGrasp: Image-based Object Grasping by Capturing Multiple Views during Robot Arm Movement with Neural Radiance Fields

The paper "RGBGrasp: Image-based Object Grasping by Capturing Multiple Views during Robot Arm Movement with Neural Radiance Fields" presents a novel grasping algorithm leveraging RGB data to achieve 3D object grasping in real-time, with special adaptations for objects exhibiting various material properties, such as transparency and specularity. The proposed method addresses noteworthy gaps in current robotic grasping research, which predominantly rely on either high-precision point-cloud cameras or a dense set of RGB images to construct rich 3D representations for achieving successful grasps.

Methodological Contributions

RGBGrasp introduces several key innovations:

1. Monocular Depth Estimation Integration: The use of pre-trained depth prediction models allows the reconstruction process to be geometrically constrained, even with sparse RGB views. By incorporating depth rank loss, the method enhances the reliability of depth estimation, effectively overcoming the limitations posed by traditional NeRF methods in environments with constrained angles.
2. Hash Encoding and Proposal Sampler Strategy: To accelerate the reconstruction of 3D scenes, the method integrates a hash encoding strategy with a novel proposal sampler network. This dual approach significantly reduces NeRF training time while maintaining high reconstruction quality.
3. Eye-on-Hand Configuration for Dynamic View Collection: As the robot's gripper approaches the object, multiple RGB views are captured, allowing the algorithm to achieve high-resolution 3D scene reconstruction despite the limited field of view. This dynamic approach allows the system to work in more constrained environments compared to methods that rely on fixed viewpoints.

Experimental Findings

The experiments conducted validate the versatility and efficacy of RGBGrasp across both simulated and real-world environments. The salient results can be summarized as follows:

• Quantitative Performance:

In scenarios with mixed materials (e.g., transparent and specular objects), RGBGrasp demonstrated superior success rates (SR) and declutter rates (DR) in grasping tasks when compared to baseline methods such as GraspNeRF and RGB-D-based methods. Notably, RGBGrasp maintained over 80% SR in various trajectory settings, significantly outperforming GraspNeRF, which showed marked performance degradation as the view angle narrowed.

• Depth Reconstruction Accuracy:

The reconstruction quality of RGBGrasp, measured in terms of depth RMSE, consistently delivered lower errors compared to GraspNeRF, especially under trajectories with reduced view angles. The method’s improved depth estimation is crucial for precise grasp pose detection and successful execution of grasps.

• Real-world Applications:

In physical robot experiments, RGBGrasp maintained high success rates, even in cluttered scenes with objects of diverse materials. The real-world evaluations underscore the method's robustness and its suitability for practical deployment in dynamic, constrained environments.

Implications and Future Directions

The proposed RGBGrasp framework has substantial practical implications for robotic manipulation tasks:

• Scalability and Flexibility:

The reliance on RGB views as opposed to specialized sensors makes the approach more scalable and adaptable to different environmental constraints. This flexibility is particularly beneficial in scenarios where space constraints prevent the use of fixed multi-view setups.

• Enhanced Perception for Complex Scenes:

By effectively integrating monocular depth estimation, RGBGrasp overcomes significant limitations related to transparent and specular objects, which frequently challenge conventional depth sensors.

The paper opens avenues for further research in the domain of robotic grasping and object manipulation. Future directions may involve:

• Enhanced Integration with Object Detection:

Combining RGBGrasp with advanced object detection algorithms could further refine grasp pose estimation, particularly in highly cluttered or occluded scenes.

• Real-time Adaptations in Dynamic Environments:

Extending the algorithm to handle dynamically moving objects by integrating motion prediction could broaden its applicability in more complex and realistic operational scenarios.

• Optimization for Edge Devices:

Exploring lightweight implementations of the hash encoding and proposal sampler strategies could facilitate deployment of RGBGrasp on edge devices with limited computational resources, enhancing its utility in field robotics applications.

In summary, RGBGrasp represents a significant advancement in the field of robotic manipulation by leveraging sparse RGB images for precise and efficient grasping, addressing key challenges posed by transparent and specular objects through innovative depth integration and acceleration techniques.