Remote Robot Control Using ML-Predicted Manipulation Parameters
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Solution Overview
Problem
Existing robotic systems face inefficiencies when tasked with performing a variety of actions on diverse components, requiring significant engineering effort and computational resources for pre-programming, and often result in idle time due to the need for human guidance, limiting productivity and operational efficiency.
Innovation Solution
Implementing a system that utilizes user interface inputs from remote client devices to control robots, generating training instances based on object manipulation parameters, and training machine learning models to predict these parameters, reducing the need for human intervention and pre-programming, and enabling robots to operate more efficiently in dynamic environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If pre-programming is used to control robots for repeated tasks, then manufacturing precision and reliability are improved, but device complexity and loss of time increase due to significant engineering effort and computational resources required
Solution Approach 1:
The system enables robots to learn and determine object manipulation parameters autonomously through machine learning models, eliminating the need for extensive pre-programming by engineers. The robot serves itself by automatically adapting to new objects and tasks through the trained model.
Solution Approach 2:
The system performs preliminary training of machine learning models using training instances generated from sensor data and expert demonstrations before actual robot operation. This pre-training phase prepares the model to quickly adapt to new tasks without requiring time-consuming on-site programming.
2Adaptability or versatility
If human guidance is solicited for robot operations, then adaptability to diverse components is improved, but productivity decreases due to idle time while awaiting human guidance
Solution Approach 1:
The system replaces the mechanical interaction of human operators physically guiding robots with an intelligent system based on machine learning models. The trained model automatically determines object manipulation parameters from sensor data, substituting human cognitive processing with an automated AI system that operates without idle time.
Solution Approach 2:
The system continuously receives feedback from sensor data during robot operations and uses this feedback to refine and update the machine learning model. This closed-loop feedback mechanism enables the robot to adapt to diverse components autonomously without requiring human intervention while maintaining high productivity.
3Reliability
If extensive pre-programming is implemented for new environments, then reliability is improved, but loss of time and productivity decrease due to significant engineering effort required before deployment
Solution Approach 1:
The system performs preliminary training of machine learning models using synthetic training instances and expert demonstrations before actual robot deployment in new environments. This pre-training phase ensures the robot has foundational knowledge to operate reliably from day one without requiring extensive on-site programming and adjustment.
Solution Approach 2:
The system implements dynamic adaptation where the machine learning model continuously learns and updates its parameters based on real-world sensor data and operational feedback. This dynamic learning capability allows the robot to maintain reliability in new environments by adapting to specific environmental characteristics rather than relying on static pre-programming.
Data Source
AI summary
Utilization of user interface inputs, from remote client devices, in controlling robot(s) in an environment. Implementations relate to generating training instances based on object manipulation parameters, defined by instances of user interface input(s), and training machine learning model(s) to predict the object manipulation parameter(s). Those implementations can subsequently utilize the trained machine learning model(s) to reduce a quantity of instances that input(s) from remote client device(s) are solicited in performing a given set of robotic manipulations and/or to reduce the extent of input(s) from remote client device(s) in performing a given set of robotic operations. Implementations are additionally or alternatively related to mitigating idle time of robot(s) through the utilization of vision data that captures object(s), to be manipulated by a robot, prior to the object(s) being transported to a robot workspace within which the robot can reach and manipulate the object.