Remote Device Control With Real-Time Human-Guided ML Adaptation
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Solution Overview
Problem
Fully automatic control of devices such as robots in sandy environments like beaches or farmland is hindered by changing environmental conditions, leading to inaccuracies and inefficiencies in tasks like weed removal or garbage collection.
Innovation Solution
A method of real-time human-operated control of remote devices using a machine learning algorithm trained on-the-fly with user input, combining human and algorithmic inputs to enhance accuracy and precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If fully automatic control is used, then device operation is automated, but accuracy and reliability deteriorate in changing environmental conditions
Solution Approach 1:
The patent combines automatic algorithmic control with manual human control into a hybrid system. The machine learning algorithm provides automated suggestions for locations of interest, while the human operator makes final decisions, merging the speed and pattern recognition of algorithms with the adaptability and judgment of human operators in changing environmental conditions.
Solution Approach 2:
The system implements feedback by using human operator inputs as training data to continuously improve the machine learning algorithm. The algorithm learns from corrected and verified location data provided by operators, creating a feedback loop that enhances accuracy over time while maintaining automated operation.
2Reliability
If manual control is used, then accuracy and reliability improve, but productivity and time consumption worsen
Solution Approach 1:
The patent merges manual control precision with automated efficiency by having the machine learning algorithm generate initial suggestions for locations of interest, which are then quickly verified or corrected by human operators. This hybrid approach maintains high accuracy while significantly reducing the time required compared to purely manual control.
Solution Approach 2:
The machine learning algorithm performs preliminary action by pre-identifying potential locations of interest before human review. This preliminary filtering reduces the workload on operators and accelerates the overall process, as they only need to verify or correct algorithm suggestions rather than analyze all possible locations from scratch.
3Loss of time
If algorithm is trained offline, then training time is consumed beforehand, but real-time adaptation to environmental changes worsens
Solution Approach 1:
The system maintains continuous useful action by training the machine learning algorithm in real-time using feedback from human operators. Instead of periodic offline training, the algorithm continuously learns from new data, ensuring it remains adapted to current environmental conditions while the device operates.
Solution Approach 2:
The machine learning algorithm performs self-service by automatically updating its own parameters and structure using training data derived from human operator inputs. The system self-improves without requiring external retraining interventions, continuously adapting to environmental changes through ongoing operation.
4Measurement precision
If high quality training data is collected manually, then algorithm accuracy improves, but cost and time consumption worsen
Solution Approach 1:
The patent merges automated data generation with manual data verification. The machine learning algorithm automatically generates initial training data suggestions, which are then quickly verified and corrected by human operators. This combination produces high-quality training data much more efficiently than purely manual collection, as operators only need to review and correct algorithm suggestions rather than collect all data from scratch.
Data Source
AI summary
A method is provided of real-time controlling a remote device to perform a task, the method comprising steps of: for controlling the remote device to perform a task, obtaining graphical data, such as image frames forming a video, of surroundings of the remote device, such as an area of farmland or beach, sending the graphical data to a remote operation device, obtaining user input data from an operator, which user input data is indicative of a location of interest in the graphical data, generating a control signal for controlling the remote device to perform a task based on the user input data, and using the control signal for controlling the remote device to perform the task at the location of interest. The user input data is further used as training data for training a machine learning algorithm, which algorithm is arranged for generating at least part of a control signal for controlling the remote device; and/or providing a suggested location of interest to the operator.