Robot-Egocentric GUI Control for Semi-Autonomous Training
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Solution Overview
Problem
Existing robot training methods, particularly for autonomous control, are costly and impractical due to the need for physical rigs and 1:1 pilot-to-robot ratios, and teleoperation systems require complex interfaces that limit accessibility.
Innovation Solution
A control system utilizing a cognitive architecture with a graphical user interface (GUI) that allows a human operator to control a robot semi-autonomously by selecting instructions from a pre-determined set based on sensor data, enabling training in both physical and simulated environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If teleoperation systems are used to control robots, then the robot can be operated through sophisticated sensors and equipment, but the interface becomes very elaborate and complicated, requiring full attention from the pilot and limiting accessibility
Solution Approach 1:
The patent extracts the complex sensor and control equipment from the pilot's direct control interface. Instead of requiring the pilot to directly manipulate sophisticated sensors and control equipment, the system presents a simplified graphical user interface that extracts only the essential control functions, separating the complexity of the underlying system from the simplicity of the user interface.
Solution Approach 2:
The patent creates a virtual copy of the robot's sensor data and control functions through the graphical user interface. The GUI presents a simplified representation (copy) of the robot's environment and control capabilities, allowing pilots to interact with a simplified model rather than the complex actual system, thus reducing interface complexity while maintaining control functionality.
2Reliability
If analogical control training is used, then the robot can be trained through physical pilot rig, but significant hardware and 1:1 pilot-to-robot ratio are required, making it costly and impractical
Solution Approach 1:
The patent uses virtual copies of robots in simulated environments for training. Instead of requiring physical pilot rigs and actual physical robots for training, the system creates virtual replicas that replicate the training experience. This allows multiple virtual robots to be trained simultaneously with the same pilot, eliminating the need for 1:1 hardware ratios and significant hardware investments while maintaining training effectiveness through realistic simulation.
Solution Approach 2:
The patent changes the physical parameters of the training environment from real-world physical robots to virtual simulated robots. By transforming the training domain from physical to digital, the system eliminates hardware constraints and allows flexible scaling of training scenarios without increasing physical hardware requirements or pilot-to-robot ratios.
3Productivity
If intermediate level training is used, then costs and time are reduced by eliminating pilot rig, but the training needs to be as similar to fully autonomous control as possible to be effective
Solution Approach 1:
The patent changes the control interface parameters from simplified GUI commands to autonomous decision-making patterns. By carefully designing the GUI to present only the essential control parameters that autonomous systems would use, the training maintains fidelity to fully autonomous control while benefiting from the cost and time efficiencies of intermediate training. The GUI presents simplified versions of the same decision-space that autonomous systems navigate.
Data Source
AI summary
Control systems for controlling operation of a robot, as well as computer program products and methods thereof are provided herein, The control system comprises a robot, including a plurality of sensors configured to convert information from the environment and the robot into sensor data and a plurality of actuators; a cognitive architecture control system, communicatively coupled to the robot, configured to control the robot, to receive the sensor data, to generate a robot-egocentric model of the environment from the sensor data, and to output autonomous actuator data to the plurality of actuators of the robot, based on at least one instruction; and a graphical user interface configured to display a graphical representation of the robot-egocentric model to a human operator and enable the human operator to select the at least one instruction from a pre-determined instruction set, based on the robot-egocentric model, to control the robot semi-autonomously.