Robotic Control Plan Templates With Machine-Learned Parameters
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Solution Overview
Problem
Existing software control systems for physical devices, such as robots, require extensive manual configuration by skilled engineers, making it time-intensive, labor-intensive, and costly to generate new robotic control plans for different applications, especially in new environments where pre-trained parameters may not be effective.
Innovation Solution
A system that processes template robotic control plans to automatically generate specific control plans for various robotics applications using machine learning procedures, allowing for configuration across multiple tasks, environments, and components, and enabling non-expert users to create plans by defining learnable parameters and executing machine learning procedures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual configuration by skilled engineers is used to generate robotic control plans, then the control plans can be precisely tailored to specific tasks, but the process becomes time-intensive, labor-intensive, and costly
Solution Approach 1:
The system enables non-expert users to generate robotic control plans through automated machine learning procedures. The template robotic control plan automatically adapts to specific applications by learning from demonstrations and executing ML procedures, eliminating the need for skilled engineers to manually configure each plan while maintaining high precision through the structured template framework
Solution Approach 2:
The system uses machine learning to automatically determine optimal parameter values for robotic control plans. By executing ML procedures that learn from task demonstrations, the system dynamically adjusts parameters such as motion trajectories, timing, and control settings to achieve task-specific precision without manual engineering for each application
2Productivity
If pre-trained parameters are used for robotic control plans, then deployment is faster, but performance in new environments deteriorates
Solution Approach 1:
The system performs preliminary machine learning training in advance to generate adapted control plans for new environments. By executing ML procedures before deployment that learn from task demonstrations in the specific environment, the system prepares optimized parameters ahead of time, enabling both fast deployment and high adaptability to new conditions
Solution Approach 2:
The system incorporates feedback mechanisms where machine learning procedures learn from task demonstrations and environmental responses. This feedback loop allows the control plan to adapt to new environments by continuously improving parameter values based on observed performance, ensuring both rapid deployment and environment-specific optimization
3Manufacturing precision
If closed software modules configured for highly-specialized tasks are used, then task-specific performance is optimized, but the system lacks flexibility for other applications
Solution Approach 1:
The system uses a universal template robotic control plan that can be configured for multiple different robotic applications through machine learning. The same template framework serves as the foundation for diverse tasks by automatically adapting its parameters through ML procedures, eliminating the need for separate closed software modules for each specialized task while maintaining high task-specific performance
Solution Approach 2:
The system transforms static, task-specific control modules into dynamic, adaptable plans. By incorporating machine learning procedures that can learn and adjust parameters for different tasks, the control plan becomes dynamic in its configuration, allowing it to optimize for each specific task while maintaining a universal structure that works across multiple applications
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using learnable robotic control plans. One of the methods comprises obtaining a learnable robotic control plan comprising data defining a state machine that includes a plurality of states and a plurality of transitions between states, wherein: one or more states are learnable states, and each learnable state comprises data defining (i) one or more learnable parameters of the learnable state and (ii) a machine learning procedure for automatically learning a respective value for each learnable parameter of the learnable state; and processing the learnable robotic control plan to generate a specific robotic control plan, comprising: obtaining data characterizing a robotic execution environment; and for each learnable state, executing, using the obtained data, the respective machine learning procedures defined by the learnable state to generate a respective value for each learnable parameter of the learnable state.