GraspNeRF: Multiview-based 6-DoF Grasp Detection for Transparent and Specular Objects Using Generalizable NeRF (2210.06575v3)

Abstract: In this work, we tackle 6-DoF grasp detection for transparent and specular objects, which is an important yet challenging problem in vision-based robotic systems, due to the failure of depth cameras in sensing their geometry. We, for the first time, propose a multiview RGB-based 6-DoF grasp detection network, GraspNeRF, that leverages the generalizable neural radiance field (NeRF) to achieve material-agnostic object grasping in clutter. Compared to the existing NeRF-based 3-DoF grasp detection methods that rely on densely captured input images and time-consuming per-scene optimization, our system can perform zero-shot NeRF construction with sparse RGB inputs and reliably detect 6-DoF grasps, both in real-time. The proposed framework jointly learns generalizable NeRF and grasp detection in an end-to-end manner, optimizing the scene representation construction for the grasping. For training data, we generate a large-scale photorealistic domain-randomized synthetic dataset of grasping in cluttered tabletop scenes that enables direct transfer to the real world. Our extensive experiments in synthetic and real-world environments demonstrate that our method significantly outperforms all the baselines in all the experiments while remaining in real-time. Project page can be found at https://pku-epic.github.io/GraspNeRF

• The paper introduces an end-to-end 6-DoF grasp detection framework using a generalizable NeRF with sparse multiview inputs.
• The method constructs a truncated signed distance function grid to improve geometric modeling and achieves over 20% enhanced grasp success in cluttered scenes.
• The differentiable architecture operates with only RGB inputs, enabling real-time grasping on transparent and specular objects and opening new research avenues.

Overview of "GraspNeRF: Multiview-based 6-DoF Grasp Detection for Transparent and Specular Objects Using Generalizable NeRF"

The paper "GraspNeRF: Multiview-based 6-DoF Grasp Detection for Transparent and Specular Objects Using Generalizable NeRF" introduces a novel approach to robotic grasping leveraging a generalizable neural radiance field (NeRF) for detecting six degrees of freedom (6-DoF) grasps on transparent and specular objects using multiview RGB inputs. The authors address a significant challenge in robotic vision systems: the inefficacy of depth sensors in capturing the geometry of transparent and specular surfaces, which are frequently encountered in real-world environments.

Methodology

The core innovation of the paper is GraspNeRF, a 6-DoF grasp detection framework that contrasts with previous methods utilizing depth images. Existing NeRF-based techniques often require dense image inputs and per-scene optimizations, which are computationally intensive and unsuitable for real-time applications. GraspNeRF circumvents these limitations by adopting a generalizable NeRF that constructs radiance fields with sparse input views, enabling zero-shot scene rendering without scene-specific training.

The paper proposes an end-to-end framework combining scene representation construction with volumetric grasp detection. The scene representation module creates a truncated signed distance function (TSDF) grid, facilitating accurate geometry representation necessary for reliable grasp detection. This representation is further optimized for grasping, enhancing the interoperability between scene understanding and grasp detection.

The synthetic dataset used for training is substantial, comprising 2.4 million images across 100,000 scenes with various object materials, fostering robust model learning and generalization to real-world scenarios. The use of domain randomization techniques in dataset generation is a crucial component, allowing the model to bridge the sim-to-real gap effectively.

Key Findings and Implications

Experimental results are robust, demonstrating that GraspNeRF significantly surpasses existing baselines, achieving over 20% improvement in grasp success rates in cluttered environments. The paper underscores an 82.2% success rate in packed scenes and a notable performance even within complex scenarios involving specular and transparent materials.

The architecture supports end-to-end differentiability, thus facilitating smooth integration of the GraspNeRF with robotic systems. This feature is vital for adapting to dynamic environments where scene configurations may change post-grasping.

The authors posit that enforcing a NeRF rendering loss during training enhances the grasp detection performance, a synergy that suggests a potentially fruitful area for future research. The implications for real-time robotic systems are significant, as the framework excels in scenarios with only RGB inputs, promoting material-agnostic robotic operations.

Future Directions

The research opens avenues for further exploration into the intersection of NeRFs and robotic grasping, especially regarding resolution improvements of the volume grid representation and enhancing geometric reconstructions. Integrating advanced geometric learning could further ameliorate grasping accuracy on challenging materials.

Continued exploration in this domain might also focus on refining the dataset to encompass a wider variety of objects and environmental factors, enhancing the robustness of the GraspNeRF framework in diverse operational contexts.

In conclusion, the GraspNeRF framework represents a substantial step forward in vision-based robotic systems, particularly in scenarios where traditional depth sensing fails. Its generalizability, coupled with the ability to process sparse inputs in real time, positions it as a compelling approach in the field of robotic manipulation.