Robot Object Learning Motion for Low-Confidence Recognition
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Solution Overview
Problem
Robotic systems face challenges in accurately identifying objects due to failures in edge detection and object recognition, particularly when objects are of similar color, translucent, or new, leading to inefficient operation and potential collisions or grasping errors.
Innovation Solution
An object identification training method for robotic devices involves a human operator sending location and identifier data to the robot, which moves a visual sensor along a predetermined learning motion path to capture additional data, allowing for improved object recognition through machine learning model training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the robotic system uses automated object recognition algorithms, then operational efficiency is improved, but object identification accuracy deteriorates for similar-colored or translucent objects
Solution Approach 1:
A human operator serves as an intermediary between the robotic system and the object recognition process. When the robot encounters difficulty identifying an object (similar color, translucent, or new object), the system automatically requests human verification through a remote computing device. The human operator views captured images and provides confirmation or correction, bridging the gap between automated efficiency and human accuracy.
Solution Approach 2:
The system implements a feedback loop where object recognition results are evaluated for confidence levels. When confidence is below a threshold, the system feeds back to the human operator for verification. This feedback mechanism allows the robot to maintain high operational efficiency for routine objects while achieving high accuracy for challenging objects through human input.
2Measurement precision
If the robotic system collects more image data for training, then object recognition accuracy is improved, but data collection time increases
Solution Approach 1:
The system performs preliminary automated object recognition and filtering before human verification. The robot captures images and pre-processes them to identify only those objects that require human attention (low confidence matches). This preliminary action reduces the overall data collection time by avoiding manual verification for all objects, focusing human effort only on challenging cases.
Solution Approach 2:
Instead of collecting complete datasets for all object types, the system uses partial human verification only when needed. The robot autonomously handles high-confidence recognitions while selectively engaging human operators for partial verification of uncertain cases. This partial action approach achieves sufficient accuracy without the time cost of exhaustive data collection.
Data Source
AI summary
A method includes receiving, by a control system of a robotic device, data about an object in an environment from a remote computing device, where the data comprises at least location data and identifier data. The method further includes, based on the location data, causing at least one appendage of the robotic device to move through a predetermined learning motion path. The method additionally includes, while the at least one appendage moves through the predetermined learning motion path, causing one or more visual sensors to capture a plurality of images for potential association with the identifier data. The method further includes sending, to the remote computing device, the plurality of captured images to be displayed on a display interface of the remote computing device.