Implicit Collision Functions for Point-Cloud Object Rearrangement
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Solution Overview
Problem
Determining collisions between objects and obstacles in robotic rearrangement tasks is challenging when object models are difficult to obtain.
Innovation Solution
A scene collision network using neural networks processes point cloud data to determine collision-free trajectories for robotic object rearrangement, utilizing multi-layer perceptrons, voxelization, and convolution operations to encode scene and object features, and classify potential paths for collision detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional collision detection methods are used with object models, then collision accuracy is improved, but the complexity of obtaining and processing object models increases system complexity
Solution Approach 1:
The patent replaces traditional geometric model-based collision detection with a neural network-based implicit function approach. Instead of using explicit object models and geometric algorithms, the system learns collision boundaries implicitly from point cloud data, substituting mechanical/geometric computation with learned representations that reduce system complexity while maintaining accuracy
Solution Approach 2:
The patent creates implicit copies of object collision boundaries through neural network learned functions. Rather than requiring explicit 3D models of objects, the system learns to represent collision surfaces as implicit functions from point cloud samples, enabling collision detection without direct access to object models
2Measurement precision
If detailed object models are obtained for collision detection, then collision detection accuracy is improved, but the time required for model acquisition and processing increases
Solution Approach 1:
The patent performs preliminary learning of collision boundaries offline using point cloud data before actual collision detection is needed. The neural network is trained in advance to recognize collision surfaces, so that during runtime, collision queries can be answered quickly without requiring real-time model acquisition or complex geometric computations
Solution Approach 2:
The system substitutes time-consuming geometric model processing with efficient neural network inference. Once the implicit collision function is learned, collision detection reduces to evaluating the learned function at query points, which is computationally much faster than traditional model-based methods
Data Source
AI summary
Apparatuses, systems, and techniques for determining whether collisions will occur in potential paths of an object within a scene. In at least one embodiment, one or more neural networks determine whether collisions will occur in potential paths of an object within a scene based at least in part on point cloud data of the object and the scene.