Bayesian Estimation Controller for Active Social Robot Interaction
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Solution Overview
Problem
Current models for real-time social interaction and contingency detection in robotics are limited by their passive design, failing to address timing and uncertainty effectively, and do not optimize for long-term information maximization, leading to greedy and non-causal decision-making.
Innovation Solution
The development of an Infomax control framework that utilizes stochastic optimal control to maximize information exchange between a robot and its environment, allowing for active decision-making and optimal behavior scheduling based on Bayesian estimation and reinforcement signals, enabling the robot to detect social agents and adapt to uncertainty and timing constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If passive information transmission models are used, then information can be sent to the next processing stage, but the system cannot actively schedule behavior to discover hypotheses quickly
Solution Approach 1:
The system transitions from static passive information transmission to dynamic active processing where neurons can rapidly increase in number and be scheduled to discover relationships with the world through feedback connections, enabling adaptive behavior scheduling
Solution Approach 2:
Feedback connections are introduced to allow neurons to discover relationships with the world through active processing rather than simple forward transmission, enabling the system to schedule behavior based on discovered hypotheses
2Measurement precision
If one arm is pulled multiple times in the two-armed bandit problem, then determination of superiority can be made, but this approach fails in contingency determination where both arms must be pulled at least once
Solution Approach 1:
The system changes the parameter of arm selection strategy from greedy single-arm exploitation to exploratory dual-arm sampling, adjusting the exploration-exploitation balance to ensure both arms are pulled at least once for contingency determination
3Productivity
If greedy Infomax models are used, then immediate information return is maximized, but long-term information maximization is not achieved
Solution Approach 1:
The system performs preliminary exploratory actions to gather information about contingencies before exploiting known relationships, ensuring long-term information maximization by preparing the system for future efficient processing
Solution Approach 2:
The system maintains continuous information-gathering behavior through scheduled neuronal processing that balances immediate returns with long-term learning, ensuring uninterrupted accumulation of useful information about environmental contingencies
4Productivity
If traditional motion control models are used, then optimization can be achieved, but the models cannot be applied to social action with greater time and uncertainty scales
Solution Approach 1:
The Infomax control framework is designed to be universal, capable of handling both traditional motion control optimization and social action problems with greater time and uncertainty scales through the same information-maximization principle
Data Source
AI summary
The present invention provides an interaction device adapted for setting own controller for maximizing expectation of information defined between a hypothesis about an interaction object and own input/output. Thus, the social robot can judge by using only simple input/output sensor whether or not the human being is present or absent at the outside world.