Robotic Control Consistency Fusion for Unseen Situations
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Solution Overview
Problem
Existing robotic control strategies face challenges in generalizing to unseen control situations or handling low-quality input data, leading to potentially unsafe outcomes.
Innovation Solution
A method for controlling robotic devices that involves training a control strategy, assessing environmental information for consistency using probabilistic measures, and combining these measures to determine safe control actions through subjective logic fusion, Dempster-Shafer belief masses, or Bayesian inference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a control strategy is trained on training data and used for unseen control situations, then the control strategy can generalize to new situations, but it may provide poor results when faced with control situations outside the training distribution or with low-quality input data
Solution Approach 1:
The system implements feedback by continuously monitoring the consistency of current sensor information with training data distributions and previous predictions. When inconsistency is detected (indicating the robot is in an unfamiliar or uncertain situation), the system adjusts its behavior by switching to a safe mode, creating a closed-loop safety mechanism that responds to environmental uncertainty
Solution Approach 2:
The system performs preliminary actions by pre-training the control strategy on comprehensive training data and establishing consistency checks before executing control actions in uncertain situations. The safe mode is prepared in advance as a fallback option, allowing the system to respond reliably when faced with unseen control situations
2Reliability
If multiple consistency measures are calculated and combined to assess environmental information reliability, then control safety in uncertain situations is improved, but the computational complexity and processing time increase
Solution Approach 1:
The consistency assessment is segmented into multiple independent measures: consistency with training data distribution, consistency with previous predictions, and consistency among different sensor inputs. Each measure is calculated separately and then combined, allowing the system to identify specific sources of uncertainty and process them in a modular fashion
Solution Approach 2:
Multiple consistency measures are merged into a unified assessment framework. The system combines the first consistency measure (comparing current situation to training data) with second consistency measures (comparing to previous predictions and other sensor inputs) to form a comprehensive reliability assessment, enabling robust safety decisions through aggregated evidence
Data Source
AI summary
A method for controlling a robotic device. The method includes: training a control strategy for the robotic device based on a plurality of training control situations and, for each of a plurality of control time points, ascertaining information about an environment of the robotic device for the control time point; ascertaining a first consistency measure for the information ascertained for the control time point, by comparing a control situation specified by the information ascertained for the control time point, to the training control situations; ascertaining at least one second consistency measure for the information ascertained for the control time point; ascertaining a combined consistency measure by combining the first consistency measure with the at least one second consistency measure; and ascertaining one or more control actions for the robotic device depending on the combined consistency measure.