Robotic Platform With On-Demand Intelligence for Unstructured Tasks
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Solution Overview
Problem
Traditional automation systems are brittle and costly, making them unsuitable for handling arbitrary objects in unstructured environments, leading to inefficiencies in tasks like pick and place, and relying on human workers who are expensive and unpredictable.
Innovation Solution
A robotic platform with an on-demand intelligence component that utilizes a pool of remote human workers to execute unsolved tasks, allowing robots to handle arbitrary objects by providing answers to structured queries, reducing costs and improving speed, versatility, and flexibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional automation systems are used to handle objects in structured environments, then manufacturing precision and reliability are improved, but adaptability to unstructured environments and arbitrary objects deteriorates
Solution Approach 1:
The system dynamically adapts its intelligence composition based on task requirements. Robots can switch between operating with full human intelligence support, partial support, or independently using trained ML models. This dynamic adjustment allows the system to maintain high precision for routine tasks while adapting to handle arbitrary objects in unstructured environments when needed.
Solution Approach 2:
The robotic platform is designed with multi-functionality to operate in both structured and unstructured environments. By integrating human workers, ML models, and robotic systems into a unified platform, it can universally handle diverse tasks ranging from precision manufacturing to arbitrary object manipulation, eliminating the need for environment-specific systems.
2Productivity
If traditional automation systems are deployed, then productivity is improved for specific tasks, but device complexity and cost increase
Solution Approach 1:
The intelligence component is segmented into distinct layers: human workers handling complex reasoning and decision-making, ML models handling pattern recognition and prediction, and robotic systems handling physical execution. This segmentation allows each component to be optimized independently and scaled according to specific task requirements, reducing overall system complexity while maintaining high productivity.
Solution Approach 2:
ML models serve as intermediaries between human workers and robotic systems. They translate human intent into robotic commands and filter sensor data for human analysis, reducing the complexity of direct human-robot interaction while maintaining high productivity through automated mediation.
3Adaptability or versatility
If human workers are used to operate robots, then adaptability to arbitrary objects is improved, but cost and reliability deteriorate
Solution Approach 1:
The system progressively replaces human mechanical operation with automated ML-based decision-making. ML models trained on data from human workers gradually take over the adaptability function, maintaining versatility while improving reliability by eliminating human factors such as fatigue, error, and unpredictability in routine operations.
Solution Approach 2:
The system implements continuous feedback loops where human worker decisions and robot outcomes are captured as training data for ML models. This feedback mechanism allows the system to learn from human versatility while progressively automating reliable decision-making, simultaneously improving both versatility and reliability over time.
4Manufacturing precision
If classical computer vision or supervised machine-learning techniques are used, then manufacturing precision is improved for known objects, but adaptability to arbitrary objects deteriorates
Solution Approach 1:
The system merges classical computer vision techniques with supervised and unsupervised machine learning approaches. Classical vision methods provide precise measurements for known objects, while ML components handle arbitrary object recognition and classification. This combination maintains manufacturing precision for structured tasks while extending versatility to unstructured environments.
Data Source
AI summary
The present disclosure relates generally to robotic systems, and more specifically to systems and methods for a robotic platform comprising an on-demand intelligence component. An exemplary computer-enabled method for operating a robot comprises obtaining an instruction for the robot, wherein the instruction is associated with a first user; identifying, based on the instruction, a task; transmitting the task to the robot; receiving, from the robot, a request associated with the task; determining whether the request can be solved by one or more trained machine-learning algorithms; if the request cannot be solved by the one or more trained machine-learning algorithms, transmitting a query to a second user's electronic device; receiving a response to the query from the second user; and causing the task to be performed by the robot based on the response