Learning Apparatus for Autonomous Robot Control
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Solution Overview
Problem
Existing robotic control systems require continuous user input and rely on user experience, which can be inadequate when dealing with rapid changes in dynamics or environments, such as unexpected obstacles, limiting their autonomy and efficiency.
Innovation Solution
A learning apparatus that records and transmits remote control signals, allowing the robotic device to perform tasks autonomously by storing environmental state data and using it to generate control signals without continuous user input, utilizing a transceiver and processor to associate sensory input with control commands and transmit them to the robotic device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If remote control relies on continuous user input and user experience, then the system can handle simple tasks, but it becomes inadequate when dynamics or environment change rapidly
Solution Approach 1:
The system performs preliminary actions by recording control signals and environmental state information during a training phase before actual operation. The learning apparatus stores associations between environmental states and control signals in advance, so that during autonomous operation, the robotic device can retrieve and execute appropriate control signals without requiring real-time user input, thereby adapting to environmental changes independently.
2Productivity
If the system requires user attention during task execution, then control can be adjusted in real-time, but productivity decreases due to continuous monitoring
Solution Approach 1:
The learning apparatus enables the robotic device to serve itself by autonomously selecting and executing control signals based on recorded environmental state associations. The system independently processes sensory input, retrieves appropriate control signals from its memory, and executes tasks without requiring continuous user monitoring or intervention, thereby significantly improving productivity and eliminating the time loss associated with constant user attention.
3Reliability
If remote control uses traditional methods with user experience dependency, then implementation is simple, but the system cannot handle unexpected obstacles effectively
Solution Approach 1:
The system implements feedback by continuously monitoring environmental state information through sensors and comparing it with stored state-control associations. When the robotic device encounters unexpected obstacles or environmental changes, the feedback mechanism retrieves appropriate pre-recorded control signals that have been associated with similar environmental states during training, enabling reliable handling of unexpected situations without requiring complex real-time decision-making algorithms.
Data Source
AI summary
Robotic devices may be operated by users remotely. A learning controller apparatus may detect remote transmissions comprising user control instructions. The learning apparatus may receive sensory input conveying information about robot's state and environment (context). The learning apparatus may monitor one or more wavelength (infrared light, radio channel) and detect transmissions from user remote control device to the robot during its operation by the user. The learning apparatus may be configured to develop associations between the detected user remote control instructions and actions of the robot for given context. When a given sensory context occurs, the learning controller may automatically provide control instructions to the robot that may be associated with the given context. The provision of control instructions to the robot by the learning controller may obviate the need for user remote control of the robot thereby enabling autonomous operation by the robot.