Modular Neural Networks for Full-DOF Robot Grasp Search
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing neural network systems for robot grasp learning face challenges in efficiently computing full degrees of freedom grasps in cluttered environments, requiring high computational complexity and often resulting in inaccurate or time-consuming grasp predictions, especially when dealing with densely packed parts in a bin.
Innovation Solution
The method involves decoupling high-dimensional neural networks into two or more neural networks of lower input dimensions, where one network encodes grasp positional dimensions and another encodes rotational dimensions, allowing for reduced search spaces and efficient computation of optimal grasps by separating the search dimensions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single large neural network is used to compute full-DOF grasps, then the system can handle all degrees of freedom in one network, but the computational complexity increases significantly and search time becomes excessive
Solution Approach 1:
The patent divides the single large neural network into multiple smaller neural networks, each responsible for computing a specific subset of degrees of freedom (e.g., one network for position, another for orientation). This segmentation reduces the computational burden on each individual network while maintaining the ability to compute complete full-DOF grasps by combining the outputs of the smaller networks.
2Reliability
If a single large neural network is used to evaluate all grasp candidates, then comprehensive grasp evaluation is achieved, but the computation time becomes unacceptably long
Solution Approach 1:
The grasp evaluation process is segmented into multiple parallel neural networks that evaluate different aspects of grasp quality simultaneously. Each network focuses on a specific dimension (e.g., positional accuracy, rotational accuracy, collision avoidance), and their results are combined to produce the final grasp evaluation. This parallel processing significantly reduces computation time while maintaining comprehensive evaluation accuracy.
3Measurement precision
If the neural network searches the entire grasp parameter space, then the optimal grasp is guaranteed to be found, but the search space becomes computationally intractable
Solution Approach 1:
The search space is segmented into multiple independent sub-spaces, each handled by a dedicated neural network. For example, one network searches the positional parameter space while another searches the rotational parameter space. This segmentation transforms the intractable high-dimensional search into several lower-dimensional searches that can be performed efficiently in parallel, maintaining optimization accuracy while dramatically improving search efficiency.
4Adaptability or versatility
If a high-dimensional neural network is used to handle cluttered environments, then the system can process complex scenes, but the computational resources required become excessive
Solution Approach 1:
The processing of cluttered environments is divided into multiple specialized neural networks that handle different aspects of scene complexity. Each network processes a specific subset of environmental features or grasp parameters, reducing the computational energy required per network while collectively maintaining the ability to handle complex cluttered scenes effectively.
Data Source
AI summary
A method for modularizing high dimensional neural networks into neural networks of lower input dimensions. The method is suited to generating full-DOF robot grasping actions based on images of parts to be picked. In one example, a first network encodes grasp positional dimensions and a second network encodes rotational dimensions. The first network is trained to predict a position at which a grasp quality is maximized for any value of the grasp rotations. The second network is trained to identify the maximum grasp quality while searching only at the position from the first network. Thus, the two networks collectively identify an optimal grasp, while each network's searching space is reduced. Many grasp positions and rotations can be evaluated in a search quantity of the sum of the evaluated positions and rotations, rather than the product. Dimensions may be separated in any suitable fashion, including three neural networks in some applications.