Task-Oriented 3D Reconstruction for Robotic Grasping Precision
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Solution Overview
Problem
Current approaches to 3D reconstruction of objects and environments lack efficiency and capability, particularly in dynamic environments, as they do not consider how the reconstruction will be used for robotic tasks, leading to inefficient use of computational resources and potential operational failures.
Innovation Solution
Generate task-oriented 3D reconstruction models with variable resolutions based on the specific tasks performed by autonomous machines, focusing on high-resolution areas relevant to the task and lower resolution on areas less relevant, using sensors and neural networks to optimize image capture and model generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If uniform high-resolution 3D reconstruction is applied to entire objects and environments, then measurement precision and reliability are improved, but computational complexity and memory requirements increase significantly
Solution Approach 1:
The patent applies local quality by assigning different resolution levels to different regions of the 3D reconstruction based on their importance to the robotic task. Task-critical regions (such as grasping surfaces, collision-prone areas, or regions requiring precise manipulation) are reconstructed with high resolution, while less critical regions use lower resolution. This selective approach maintains measurement precision where needed while significantly reducing overall computational complexity and memory requirements.
2Reliability
If comprehensive 3D reconstruction of entire environment is performed, then reliability of autonomous operation is improved, but loss of time for reconstruction increases
Solution Approach 1:
The patent extracts and focuses reconstruction efforts only on the portions of the environment that are relevant to the current robotic task. Rather than performing comprehensive reconstruction of the entire environment, the system identifies and reconstructs only task-critical regions (such as the immediate workspace, objects to be manipulated, or paths to be traversed). This extraction approach maintains operational reliability for the specific task while dramatically reducing reconstruction time.
3Manufacturing precision
If high-resolution reconstruction is applied uniformly, then manufacturing precision of the digital model is improved, but loss of energy for processing increases
Solution Approach 1:
The patent implements local quality by varying the reconstruction resolution across different spatial regions based on task requirements. High-resolution reconstruction is applied only to regions where precise digital models are necessary for successful task execution (such as contact surfaces, geometrically complex areas, or regions requiring accurate collision detection), while other regions use coarser resolution. This approach maintains manufacturing precision of the digital model where needed while significantly reducing computational energy consumption.
Data Source
AI summary
Autonomous operations, such as robotic grasping and manipulation, in unknown or dynamic environments present various technical challenges. For example, three-dimensional (3D) reconstruction of a given object often focuses on the geometry of the object without considering how the 3D model of the object is used in solving or performing a robot operation task. As described herein, in accordance with various embodiments, models are generated of objects and/or physical environments based on tasks that autonomous machines perform with the objects or within the physical environments. Thus, in some cases, a given object or environment may be modeled differently depending on the task that is performed using the model. Further, portions of an object or environment may be modeled with varying resolutions depending on the task associated with the model.