3D Scene Representation for Robotic Collision Detection
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Solution Overview
Problem
Existing robotic object rearrangement methods struggle with generalizing to unknown cluttered scenes, as they require complete geometric models of objects and environments, which are costly to create and not feasible in real-world scenarios. Additionally, recent neural rearrangement methods are limited to specific types of scenes that are not densely cluttered.
Innovation Solution
A method using a three-dimensional (3D) scene representation for collision detection and waypoint prediction in object rearrangement tasks. This involves processing images of a scene to learn a 3D representation, performing collision detection to sample a target object, and predicting waypoints for rearrangement tasks using the 3D representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complete geometric models are used for collision detection, then collision detection accuracy is improved, but the time and resources required to create these models increase significantly
Solution Approach 1:
The patent uses neural radiance fields (NeRF) to create a lightweight volumetric representation that copies the essential geometric and appearance properties of complex scenes without requiring complete geometric models. This volumetric representation serves as a simplified copy that enables accurate collision detection while avoiding the time-consuming process of creating detailed geometric models of all objects in the scene.
Solution Approach 2:
The patent replaces traditional geometric model-based collision detection with a neural network-based volumetric representation. Instead of using mechanical/geometric methods that require complete object models, the system uses learned continuous volumetric representations from neural radiance fields to perform collision detection, substituting the mechanical modeling approach with a data-driven neural approach.
2Reliability
If traditional collision detection methods are used, then collision detection reliability is improved, but the ability to generalize to unknown cluttered scenes deteriorates
Solution Approach 1:
The patent performs preliminary action by training neural radiance fields on observed scenes before rearrangement tasks. This pre-learning phase creates volumetric representations of the scene geometry and appearance, enabling the system to generalize to unknown cluttered scenes. The preliminary training allows the model to adapt to specific environments while maintaining reliable collision detection during actual rearrangement tasks.
Solution Approach 2:
The patent changes the fundamental parameters of collision detection by transitioning from discrete geometric model parameters to continuous volumetric representations learned by neural networks. This parameter transformation enables the system to handle unknown cluttered scenes by learning scene-specific geometric properties while maintaining reliable collision detection through the continuous differentiable nature of volumetric representations.
3Manufacturing precision
If complete object and world state information is required, then task-and-motion-planning accuracy is improved, but the complexity of the system increases
Solution Approach 1:
The patent extracts only the essential geometric and appearance information needed for collision detection and rearrangement planning from complete object and world state information. By using neural radiance fields to represent only the necessary volumetric properties, the system achieves accurate rearrangement planning without requiring or processing complete geometric models of all objects, thereby reducing system complexity.
Solution Approach 2:
The patent creates a universal volumetric representation using neural radiance fields that serves multiple functions simultaneously: collision detection, waypoint prediction, and scene understanding. This single continuous differentiable volumetric representation replaces multiple separate systems that would otherwise be needed for different tasks, reducing overall system complexity while maintaining high rearrangement task accuracy.
Data Source
AI summary
One common robotic task is the rearrangement of physical objects situated in an environment. This typically involves a robot manipulator picking up a target object and placing the target object in some target location, such as a shelf, cabinet or cubby, and requires the skills of picking, placing and generating complex collision-free motions, oftentimes in a cluttered environment. The present disclosure provides collision detection for object rearrangement using a three-dimensional (3D) scene representation.