3D Robot Imitation Learning With Neural State-Action Mapping
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Solution Overview
Problem
Existing methods for robot action imitation learning in three-dimensional spaces are less effective due to difficulties in applying state-to-action mapping strategies to 3D spatially redundant mechanical arms and the separation of skill learning, resulting in limited intelligence in action imitation.
Innovation Solution
A method utilizing a series-parallel multi-layer BP neural network to learn state and action characteristic matrices from demonstrative information, allowing robots to execute actions in three-dimensional spaces by dividing the space based on workspace requirements, obtaining joint and end position state information, and training a neural network to replicate motion strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional state-to-action mapping methods are used for 3D spatially redundant mechanical arms, then the learning process becomes difficult to apply, but the patent applies a neural network model that successfully handles 3D spatially redundant mechanical arms by learning state and action characteristic matrices
Solution Approach 1:
The patent transforms the learning problem by changing parameters from traditional state-to-action mapping to learning state and action characteristic matrices through neural networks. This parameter transformation enables the system to handle 3D spatially redundant mechanical arms effectively by representing states and actions in a matrix format that captures the complexity of multi-dimensional spatial relationships.
Solution Approach 2:
The patent replaces traditional mechanical learning methods with a neural network-based computational system. Instead of using conventional control theories and mapping methods, the system employs a neural network model that learns from demonstrative information, substituting mechanical learning approaches with an intelligent computational framework that better handles 3D spatial redundancy.
2Ease of manufacture
If skill learning is separated into individual skills, then each skill can be learned independently, but the learning process does not enhance other skills and reduces overall intelligence
Solution Approach 1:
The patent merges individual skill learning into a unified neural network model that learns state and action characteristic matrices simultaneously. Instead of training separate models for each skill, the system integrates multiple skills into a single learning framework where the neural network learns generalizable patterns from demonstrative information, enabling one skill's learning to enhance other skills through shared feature representations.
Solution Approach 2:
The patent creates a universal neural network model that can perform multiple skills through a single learning process. The state and action characteristic matrices learned by the neural network serve multiple functions across different skills, allowing the system to generalize from one skill to another and enhancing overall intelligence rather than treating each skill as an isolated task.
3Ease of operation
If hands-on demonstration is used for collecting demonstrative cases, then the data collection process is simple, but the learning efficiency and intelligence of action imitation is limited
Solution Approach 1:
The patent replaces traditional hands-on demonstration data collection with a neural network-based learning system that processes demonstrative information more efficiently. While hands-on demonstration remains the data collection method, the neural network model transforms this collected data into learned characteristic matrices that significantly improve learning efficiency and action imitation intelligence compared to traditional mapping methods.
Data Source
AI summary
The present invention provides a method for robot action imitation learning in a three-dimensional space and a system thereof, relates to the technical fields of artificial intelligence and robots. A method based on a series-parallel multi-layer backpropagation (BP) neural network is designed for robot action imitation learning in a three-dimensional space, which applies an imitation learning mechanism to a robot learning system, under the framework of the imitation learning mechanism, to train and learn by transmitting demonstrative information generated from a mechanical arm to the series-parallel multi-layer BP neural network representing a motion strategy. The correspondence between a state characteristic matrix set of the motion and an action characteristic matrix set of the motion is learned, to reproduce the demonstrative action, and generalize the actions and behaviors, so that when facing different tasks, the method does not need to carry out action planning separately, thereby achieving high intelligence.