Robot Control Training Using Sensor-Guided Autonomous Refinement
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Solution Overview
Problem
Current robotic systems require significant time to transition from human-operated to autonomous control, as they need to learn and adapt by recording and labeling traces of human-directed tasks, which limits their efficiency and adaptability in dynamic environments.
Innovation Solution
A method and system where a robot receives both human-piloted control instructions and environmental sensor data to autonomously generate and refine control instructions, allowing it to operate independently and improve its task performance over time through machine learning, enabling seamless transitions between piloted and autonomous modes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If robots learn and adapt by recording and labeling traces of human-directed tasks, then they can perform complex tasks autonomously, but the transition from human-operated to autonomous control takes significant time
Solution Approach 1:
The system performs preliminary actions by continuously recording sensor data and control instructions during human-operated modes, preparing training datasets in advance. This allows the robot to quickly transition to autonomous control when needed, as the learning data is already collected and ready for processing
Solution Approach 2:
The system implements feedback mechanisms where the robot continuously monitors its own performance and environmental sensor data, using this information to refine its control instructions. This feedback loop enables faster adaptation and reduces the time required to achieve effective autonomous operation
2Productivity
If robots operate autonomously without continuous human intervention, then efficiency improves, but adaptability to dynamic environments decreases
Solution Approach 1:
The system employs dynamic control instructions that can adapt to changing environmental conditions. The robot uses real-time sensor data to modify its behavior while maintaining autonomous operation, allowing it to respond to dynamic environments without sacrificing efficiency or requiring continuous human intervention
Solution Approach 2:
The robot performs self-service by autonomously processing sensor data and generating control instructions without human intervention. This self-service capability maintains high operational efficiency while the system's learning algorithms enable it to adapt to new environments independently
Data Source
AI summary
Robotic systems, methods of operation of robotic systems, and storage media including processor-executable instructions are disclosed herein. The system may include a robot, at least one processor in communication with the robot, and an operator interface in communication with the robot and the at least one processor. The method may include executing a first set of autonomous robot control instructions which causes a robot to autonomously perform the at least one task in an autonomous mode, and generating a second set of autonomous robot control instructions from the first set of autonomous robot control instructions and a first set of environmental sensor data received from a sensor. Execution of the second set of autonomous robot control instructions causes the robot to autonomously perform the at least one task. The method may include producing at least one signal that represents the second set of autonomous robot control instructions.