Remote Robot Control Using Predictive Object Manipulation Inputs
Find Innovative SolutionsGenerate Solutions
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 and machine learning models to predict object manipulation parameters, reducing the need for pre-programming and human intervention by using visual representations and sensor data to automate robotic tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
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 required
Solution Approach 1:
The system performs preliminary actions by capturing vision data and generating object representations in advance before the robot needs to manipulate the object. Visual representations are prepared and transmitted to remote devices ahead of time, allowing user inputs to be collected in advance, thereby reducing the robot's idle waiting time while maintaining reliable task execution.
Solution Approach 2:
The system creates visual copies (object representations) of physical objects through vision data capture and processing. These digital representations are transmitted to remote devices, allowing users to interact with copies rather than requiring direct physical programming, thus reducing the time and complexity of robot pre-programming while maintaining task reliability.
2Adaptability or versatility
If human guidance is solicited for robot tasks, then adaptability to diverse components is improved, but productivity decreases due to robot idle time while awaiting human inputs
Solution Approach 1:
The system performs preliminary actions by preparing visual representations and transmitting them to remote devices before the robot completes its current task cycle. This allows users to provide guidance inputs in advance during the robot's execution phase, eliminating idle waiting time and maintaining high productivity while preserving adaptability to diverse components.
Solution Approach 2:
The system ensures continuous useful action by overlapping the robot's task execution with the user input process. While the robot manipulates objects based on existing guidance, new visual representations are prepared and transmitted, and new user inputs are collected, ensuring that the robot never remains idle while maintaining the ability to adapt to diverse components.
3Ease of operation
If visual representations are transmitted to remote devices, then ease of operation is improved, but use of energy increases due to network transmission requirements
Solution Approach 1:
The system extracts and transmits only the essential visual information needed for robot control—specifically, processed object representations rather than complete raw vision data. This selective extraction reduces the data volume transmitted over the network, lowering energy consumption while preserving the ease of operation for remote users.
Solution Approach 2:
The visual representation transmission is segmented into discrete object representations that are processed and transmitted individually as needed. This segmentation allows for optimized transmission scheduling and data compression, reducing overall network energy usage while maintaining ease of remote operation through timely delivery of essential visual information.
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.