Robot Dynamics Model Switching for Sample-Efficient Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Training a model to accurately predict all state transitions of a cycle of actions executed by a control target, such as a robot, using a single model is inefficient and requires many attempts.
Innovation Solution
A training and control device that acquires state transition data, generates dynamics models, trains a switching model to select the appropriate dynamics model based on the current state and command, and uses this model to generate predicted command series that maximize rewards, thereby controlling the robot's actions efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single model is used to predict all state transitions of a cycle of actions, then the model structure is simple, but many attempts are required for accurate prediction
Solution Approach 1:
The patent divides the single model into multiple dynamics models, each specializing in predicting specific state transitions within a cycle of actions. The system segments the prediction task by creating specialized models for different phases or types of state transitions, thereby improving prediction accuracy while maintaining manageable complexity through modular architecture.
2Measurement precision
If multiple dynamics models are generated and a switching model is trained, then prediction accuracy improves, but the system complexity increases
Solution Approach 1:
The patent introduces a switching model as an intermediary component that coordinates between multiple dynamics models. This switching model acts as a mediator that selects or combines predictions from different dynamics models based on the current state, achieving high prediction accuracy while managing system complexity through centralized coordination rather than requiring complex interactions between all model components.
3Adaptability or versatility
If many attempts are made to train a single model, then comprehensive coverage of state transitions is achieved, but training time increases
Solution Approach 1:
The patent employs preliminary action by pre-training multiple dynamics models on specific subsets of state transitions before deployment. Each dynamics model is preliminarily trained on relevant portions of the state space, allowing the system to achieve comprehensive coverage without requiring extensive trial-and-error training of a single model. The switching model is also preliminarily trained to effectively route between the pre-trained dynamics models.
Data Source
AI summary
A training device of a training and control device generates plural dynamics model, and trains a switching model for designating a dynamics model therefrom that corresponds to a state of a robot and a command action. A control device acquires a state of the robot generates plural candidate command series for the robot, by executing the switching model input with each command contained in each candidate command series and state corresponding to each command, designates the dynamics model applicable to each command and state corresponding to the command. For each of the candidate command series, the control device generates a predicted state series using the dynamics model designated as corresponding to the commands contained in the candidate command series, generates a predicted command series predicted to maximize a reward of the predicted state series, and outputs a first command contained in the predicted command series.