Bayesian Action-Chain Control for Smooth Robotic Exploration
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Solution Overview
Problem
Reinforcement learning methods for robotic devices often result in inefficient exploration due to low temporal coherence of actions, leading to non-smooth action trajectories and potential hardware damage, as they rely on stochastic strategies like Gaussian distributions that generate independent noise for each action.
Innovation Solution
A method utilizing Bayesian inference to update probability distributions over chains of actions, ensuring temporal correlation by using a priori and a posteriori distributions to select actions, which allows for temporally coherent behavior without requiring specific domain knowledge or trained environmental models, and can be integrated with any training algorithm that supports recurrent control policies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If Gaussian distribution strategy is used to generate actions in reinforcement learning, then exploration of the environment is achieved, but temporal coherence of actions deteriorates leading to low data efficiency
Solution Approach 1:
The patent applies preliminary action by planning a chain of future actions ahead of time using Bayesian inference. Instead of selecting actions one step at a time, the system predicts a sequence of actions (action chain) that will lead to the goal state, thereby improving temporal coherence and data efficiency while maintaining exploration capability
Solution Approach 2:
The patent implements feedback through Bayesian inference that continuously updates the probability distribution over action chains based on observed states. The system uses the current state to update beliefs about future actions, creating a feedback loop that improves action coherence without sacrificing exploration
2Adaptability or versatility
If stochastic strategies with independent Gaussian noise are used for each action, then action diversity is achieved, but temporal coherence deteriorates causing non-smooth trajectories
Solution Approach 1:
The patent merges independent action selections into a unified action chain prediction. By combining multiple action predictions into a single coherent sequence generated through Bayesian inference, the system maintains action diversity while ensuring temporal coherence and smooth trajectories
Solution Approach 2:
The system performs preliminary planning of action chains before execution. By predicting a sequence of correlated actions in advance using Bayesian inference, the system ensures temporal coherence and smooth trajectories while still maintaining diversity through the probabilistic nature of the predictions
3Adaptability or versatility
If independent Gaussian noise is added to deterministic function output for each action, then exploration is enabled, but action sequences exhibit low temporal coherence leading to jerky behavior
Solution Approach 1:
The patent uses Bayesian inference as a feedback mechanism that updates the action chain prediction based on the current observed state. This feedback loop ensures that actions remain coherent and smooth while still enabling exploration through the probabilistic prediction of future actions
Solution Approach 2:
The system performs preliminary prediction of action chains before execution. By planning a sequence of correlated actions in advance using Bayesian inference, the system ensures smooth behavior while maintaining exploration capability through the inherent uncertainty in the predictions
Data Source
AI summary
A method for controlling a technical system. The method includes: ascertaining, as a first probability distribution, a probability distribution for a specification of a first chain of actions to perform based on a first state of the technical system to control the technical system; selecting an action for the first state according to the first probability distribution and controlling the technical system accordingly; ascertaining a second state which the technical system has entered by performing the first action; ascertaining by Bayesian inference, as a second probability distribution, a probability distribution for a specification of a second chain of actions to perform based on the second state; selecting an action for the second state according to the second probability distribution and controlling the technical system according to the selected action for the second state.