Towards Confidence-guided Shape Completion for Robotic Applications (2209.04300v1)

Abstract: Many robotic tasks involving some form of 3D visual perception greatly benefit from a complete knowledge of the working environment. However, robots often have to tackle unstructured environments and their onboard visual sensors can only provide incomplete information due to limited workspaces, clutter or object self-occlusion. In recent years, deep learning architectures for shape completion have begun taking traction as effective means of inferring a complete 3D object representation from partial visual data. Nevertheless, most of the existing state-of-the-art approaches provide a fixed output resolution in the form of voxel grids, strictly related to the size of the neural network output stage. While this is enough for some tasks, e.g. obstacle avoidance in navigation, grasping and manipulation require finer resolutions and simply scaling up the neural network outputs is computationally expensive. In this paper, we address this limitation by proposing an object shape completion method based on an implicit 3D representation providing a confidence value for each reconstructed point. As a second contribution, we propose a gradient-based method for efficiently sampling such implicit function at an arbitrary resolution, tunable at inference time. We experimentally validate our approach by comparing reconstructed shapes with ground truths, and by deploying our shape completion algorithm in a robotic grasping pipeline. In both cases, we compare results with a state-of-the-art shape completion approach.

• The paper introduces an implicit function-based framework with hypernetworks that generate confidence measures for each reconstructed point.
• It employs a transformer decoder and gradient-based sampling to achieve arbitrary resolution shape completion while reducing computational cost.
• Experimental validation shows improved grasp success rates and higher Jaccard similarity, emphasizing its practical impact on robotic manipulation.

Confidence-guided Shape Completion for Robotics

In the paper "Towards Confidence-guided Shape Completion for Robotic Applications," the authors address the challenge of completing 3D object shapes based on partial visual data, a common issue in robotic tasks involving manipulation and navigation. The proposed solution aims to overcome the limitations of fixed-resolution outputs in current state-of-the-art shape completion methods by introducing a novel approach using implicit 3D representations.

Methodology

The authors present an implicit function-based framework for shape completion, which provides confidence values for each reconstructed point. This method leverages a hypernetwork structure, wherein a primary network generates the weights for a secondary network tasked with performing shape completion. This architecture reduces model size and integrates a fine-tuning mechanism to enhance data usage during evaluation.

Key contributions of this work include:

1. Hypernetwork-based Architecture: The proposed model uses a transformer decoder to create an implicit 3D shape representation, allowing for shape completion at arbitrary resolutions.
2. Gradient-based Sampling Algorithm: An efficient technique that samples from the implicit function, optimizing for both resolution and computational cost.

Experimental Validation

Experimentation demonstrated the model's superior performance compared to existing approaches. The evaluation involved comparing the reconstructed shapes against ground truth data using a popular object dataset. Additionally, the integration of the shape completion algorithm into a robotic grasping pipeline showcased improved grasp success rates over previous methods.

The authors have made the code publicly accessible, promoting transparency and facilitator reproducibility of these experiments.

Results and Implications

The paper reports strong numerical results, with improvements noted in grasp success rates and Jaccard similarity measures over baseline methods. These results suggest significant practical implications for the use of deep learning-based shape completion in real-world robotic applications. Specifically, the ability to reconstruct shapes at varying resolutions while measuring reconstruction confidence can enhance the robustness and adaptability of robotic systems in unstructured environments.

The theoretical contributions also provide new insights into the application of transformers and hypernetworks in 3D data processing. The introduction of confidence measures for reconstructed points can guide future research towards using probabilistic data for improved decision-making in robotics.

Future Directions

The work opens several avenues for future research. Further exploration of the confidence measures could lead to advanced applications in robotic grasp planning and obstacle avoidance. Additionally, the integration of this shape completion approach with other robotic perception systems could lead to comprehensive solutions for autonomous navigation.

Furthermore, as AI and robotics technologies evolve, there may be opportunities to refine these methods to handle more complex and dynamic environments, potentially incorporating real-time processing capabilities for on-the-fly shape completion.

In conclusion, this paper presents a solid advancement in the domain of robotic shape completion, providing both practical tools and theoretical foundations that will likely influence subsequent developments in robotic perception frameworks.