Neural Network Modularization for High-Dimensional Robot Grasping
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Solution Overview
Problem
Existing neural network systems for robot grasping in cluttered environments face challenges such as high computational complexity, inaccurate predictions, and inability to handle interference between parts, especially in high-dimensional tasks like bin picking, due to their reliance on single large networks that encode and search all degrees of freedom simultaneously.
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 grasp rotational dimensions, allowing for separate and efficient search of each dimension, thereby reducing the search space and improving accuracy and speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single large neural network encodes and searches all degrees of freedom simultaneously, then the system can handle high-dimensional tasks, but the computational complexity increases significantly
Solution Approach 1:
The patent divides the high-dimensional neural network into multiple lower-dimensional sub-networks, each responsible for specific degrees of freedom. For example, one network handles grasp position while another handles grasp orientation, reducing the computational burden of each individual network while maintaining the ability to solve high-dimensional bin picking tasks through coordinated search.
2Manufacturing precision
If a single large neural network searches all degrees of freedom, then complete grasp planning is achieved, but the search time increases
Solution Approach 1:
The search space is segmented into multiple lower-dimensional subspaces, each searched by a dedicated neural network. This parallel search approach reduces the time required compared to searching the entire high-dimensional space sequentially, while the coordination mechanism ensures the final grasp plan remains accurate and complete.
Solution Approach 2:
The patent performs preliminary search in lower-dimensional subspaces before combining results to form the complete grasp plan. By pre-computing feasible solutions in each subspace and then coordinating them, the system reduces overall search time while maintaining grasp planning accuracy.
3Area of stationary object
If a neural network predicts grasp quality for each pixel, then comprehensive coverage is achieved, but prediction accuracy decreases due to the large number of pixels
Solution Approach 1:
Instead of predicting grasp quality for every pixel simultaneously, the patent segments the image space and uses multiple specialized networks that each handle specific regions or aspects. This segmentation allows each network to focus computational resources on its designated subspace, maintaining high prediction accuracy across the entire image coverage.
4Device complexity
If a single network handles all grasp dimensions, then the system is simpler to implement, but it cannot handle cluttered environments with interference between parts
Solution Approach 1:
The patent employs multiple specialized neural networks, each trained to handle specific aspects of cluttered environment analysis. This segmentation allows each network to develop expertise in detecting and reasoning about particular types of part interference and spatial relationships, improving overall reliability in cluttered bin picking scenarios compared to a single general-purpose network.
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.