Robotic Grasp Planning With Heuristic-Labeled Depth Images
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current robotic grasping systems face challenges in accurately training deep neural networks for grasp computations due to inefficiencies and imprecision, particularly in dynamic environments where objects are randomly configured, leading to cumbersome and imprecise grasps.
Innovation Solution
Generating synthetic datasets with heuristic-based grasp annotations in 2D depth images to train neural networks, allowing for efficient and accurate grasp computations by encoding spatial information and inter-object interactions, thereby improving grasp accuracy in diverse object configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional teach-based approaches are used for robotic grasping, then operator control is simplified, but adaptability to dynamic environments and random object configurations deteriorates
Solution Approach 1:
The patent uses synthetic depth images that replicate real-world object configurations and grasp scenarios. These synthetic images serve as virtual copies of physical environments, allowing the neural network to learn from simulated data without requiring extensive real-world teaching. The synthetic datasets include annotated grasp locations that mirror actual grasping situations, enabling the system to generalize to dynamic environments while maintaining ease of operation through automated learning rather than manual programming.
2Extent of automation
If deep neural networks are trained with current approaches, then grasp computations can be performed autonomously, but precision and efficiency of grasp computations deteriorates
Solution Approach 1:
The patent performs preliminary action by pre-computing and storing annotated synthetic depth images with grasp locations before actual grasping operations. The system generates synthetic datasets in advance that include depth information and annotated grasp points for various object configurations. During runtime, the neural network simply queries this pre-prepared knowledge base, achieving both high automation and high precision without requiring complex real-time computations.
Solution Approach 2:
The patent replaces traditional mechanical teaching approaches with a data-driven neural network system. Instead of manually programming grasp locations for each object configuration, the system uses a neural network trained on synthetic datasets to automatically determine grasp points. This substitution of mechanical teaching with intelligent algorithms achieves superior precision and maintains full automation.
3Productivity
If synthetic datasets are generated with heuristic-based grasp annotations, then training efficiency improves, but complexity of data generation process increases
Solution Approach 1:
The patent creates a universal synthetic data generation system that produces annotated depth images for multiple object types, configurations, and grasp scenarios simultaneously. The heuristic-based annotation process applies general rules that work across diverse objects and situations, making the data generation process multi-functional. This universal approach improves training efficiency by providing comprehensive datasets while managing complexity through standardized annotation heuristics that can be applied consistently across different scenarios.
Data Source
AI summary
In some cases, images and depth maps can define bins with objects in random configurations. It is recognized herein that current approaches to training deep neural networks to perform grasp computations lack capabilities and efficiencies, such that the resulting grasp computations and grasps can be imprecise or cumbersome, among other shortcomings. Synthetic depth images can be labeled with grasp annotations that are generated based on heuristic-based analyses, so as to define annotated synthetic datasets. The annotated synthetic datasets can be used to train neural networks to determine the best grasp locations for different objects arranged in a variety of positions with respect to each other.