Residual Reinforcement Learning for Adaptive Motion Control
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Solution Overview
Problem
Existing motion control optimization methods, including manual parameter selection and reinforcement learning, are inefficient and limited in their ability to adapt to different controlled objects, requiring extensive expertise and being specific to individual systems.
Innovation Solution
The proposed solution involves training an online reinforcement learning model based on a motion control model, allowing for efficient adaptation and optimization of motion control parameters, and further enhancing this process by using offline reinforcement learning models trained on cloud data to improve universality and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual parameter selection is used for motion control optimization, then motion control performance can be improved, but the process requires much time and effort and is inefficient
Solution Approach 1:
The patent replaces manual mechanical optimization processes with automated reinforcement learning algorithms. The system uses intelligent agents to automatically select and optimize motion control parameters, substituting human expert manual tuning with automated computational methods that achieve both high performance and efficiency.
Solution Approach 2:
The reinforcement learning system performs self-optimization by automatically learning optimal motion control parameters through trial and error. The algorithm independently adjusts parameters without requiring continuous human intervention, enabling the system to self-improve performance while minimizing time investment.
2Extent of automation
If reinforcement learning is used to learn optimal parameters in a motion control model, then automated optimization is achieved, but deeper field knowledge is required to model the controlled object and the performance improving effect is limited
Solution Approach 1:
The patent segments the optimization process into multiple independent reinforcement learning agents, each responsible for specific motion control parameters. This division reduces the complexity of modeling the entire controlled object by breaking it down into manageable components that can be optimized separately.
Solution Approach 2:
The system automatically adjusts motion control parameters through reinforcement learning without requiring deep field knowledge for manual modeling. The algorithm learns optimal parameter values directly from interaction with the controlled object, eliminating the need for complex analytical models and expert domain knowledge.
3Reliability
If a reinforcement learning model is trained for a specific controlled object, then optimal control is achieved for that object, but the model cannot be reapplied to another controlled object
Solution Approach 1:
The patent designs reinforcement learning models with universal architectures that can be adapted to different controlled objects. The system uses transfer learning techniques where knowledge gained from training on one object can be transferred and fine-tuned for other objects, enabling a single model framework to serve multiple applications while maintaining optimality for each specific object.
4Reliability
If online reinforcement learning training is performed from scratch, then the model learns optimal parameters, but training efficiency is low
Solution Approach 1:
The patent implements pre-training of reinforcement learning models using simulated environments or transfer knowledge from similar controlled objects before deploying to the actual system. This preliminary action provides a good initial parameter configuration that significantly reduces the training time required when the model is deployed to the real controlled object, while still achieving optimal performance.
Data Source
AI summary
Some embodiments of the teachings herein include a motion control method. An example includes: creating a motion control model; training an online reinforcement learning model using the model; producing feedback with a controlled object, a model control value, and an initial control value; calculating a reward using the control value and the feedback; generating a residual control value using the online reinforcement learning model based on the reward, the model control value, and the feedback; controlling motion of the object with the residual control value and the model control value; sending the motion control model, the model control value, the feedback, and the reward to the cloud; training an offline model with the motion control model, the model control value, the feedback value, and the reward; and updating the existing online model using the offline model or deploying the offline model in a motion control system without an online model.