Robot Control Decoder Adaptation From Biosignal Command Deviation
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Solution Overview
Problem
Existing methods for calibrating biosignal-based interfaces, such as brain-computer interfaces (BCIs), often require separate training tasks and interrupt the user's activity, leading to suboptimal control signals when transitioning to actual device control.
Innovation Solution
A method that continuously updates the decoding algorithm used in biosignal-based interfaces by comparing user commands derived from bio-signals with optimal commands calculated based on the task's geometric constraints, thereby adapting the algorithm in real-time to improve control efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If separate training tasks are used to calibrate the decoder, then the decoder can be initially configured, but the user's activity is interrupted and the control signals become suboptimal when transitioning to actual device control
Solution Approach 1:
The system performs preliminary decoder calibration during open-loop training tasks where the user generates specific biosignals. This preliminary action establishes an initial decoding model that can later be refined during closed-loop operation, reducing the need for repeated interruptions.
Solution Approach 2:
The system transitions from discrete training tasks to continuous closed-loop calibration during actual robot control operations. The decoder is continuously adapted using feedback from the robot's state and task requirements, maintaining calibration accuracy without interrupting the user's activity flow.
2Measurement precision
If closed-loop training is used to adapt the decoder, then control accuracy improves, but the system complexity increases due to requiring task definitions and optimal command calculations
Solution Approach 1:
The system implements closed-loop feedback by comparing the decoded control signals with the actual robot state and task requirements. This feedback is used to continuously adapt and refine the decoder parameters, improving control accuracy while maintaining a manageable system through iterative optimization.
Solution Approach 2:
The system dynamically adjusts decoder parameters based on task requirements and observed performance. By changing parameters such as decoding weights and thresholds during operation, the system achieves high accuracy without requiring a completely complex reconfiguration of the entire control architecture.
3Productivity
If the decoder is continuously adapted during operation, then control efficiency improves, but the computational load increases
Solution Approach 1:
The system performs partial adaptation of the decoder during operation, focusing computational resources on adjusting only the most critical parameters that have the greatest impact on control accuracy. This selective approach maintains high efficiency while reducing overall computational load compared to complete re-calibration.
Solution Approach 2:
The continuous adaptation is implemented periodically rather than continuously, with the decoder updated at specific intervals or trigger events during robot operation. This periodic approach maintains control efficiency while significantly reducing computational energy consumption compared to constant real-time adaptation.
Data Source
AI summary
The invention relates to a method for controlling a robot, said method including the following method steps: a) detecting a biosignal on a body part of a user; b) deriving a user command ({dot over (x)}usr) for controlling the robot from the detected biosignals; c) deriving a task to be performed from the user command ({dot over (x)}usr) using an intention recognition module; d) calculating an optimal command ({dot over (x)}opt) which is most efficient for performing the task; e) determining a deviation between the user command ({dot over (x)}usr) and the optimal command ({dot over (x)}opt); f) adjusting the decoding algorithm, by means of which the user command ({dot over (x)}usr) is decoded from the biosignal, based on the determined deviation; and g) controlling the robot using the adjusted decoding algorithm.