Robot Task Learning From Demonstrations With Neural Plan Generation
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Solution Overview
Problem
Conventional approaches for programming robotic devices are time-consuming and costly, making them financially prohibitive for many users, as they require significant programming and testing time, limiting the ability to perform multiple tasks or make changes.
Innovation Solution
A system that enables robots to learn human-readable plans from real-world demonstrations using neural networks for perception, program generation, and execution, allowing robots to infer actions and relationships between objects, facilitating quick and cost-effective task training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional programming approaches are used to program robotic devices, then the robot can perform tasks with high reliability, but the programming time and cost increase significantly
Solution Approach 1:
The system captures real-world demonstrations of tasks and creates a digital copy of the human performance. Neural networks process this demonstration data to generate executable robot programs, effectively copying human skill acquisition rather than programming it traditionally. This reduces programming time while maintaining reliability through the structured learning process.
Solution Approach 2:
The patent replaces manual programming mechanics with automated machine learning mechanics. Instead of a programmer manually writing code, neural networks automatically learn task execution patterns from demonstration data and generate programs, substituting the mechanical programming process with an automated learning system that reduces time investment.
2Manufacturing precision
If conventional programming approaches are used, then the robot can execute tasks accurately, but the cost of programming and testing becomes financially prohibitive
Solution Approach 1:
By copying human demonstrations rather than requiring expert programmers to write code, the system eliminates the need for expensive programming expertise. The neural networks process demonstration data to achieve accurate task execution, replacing costly manual programming with automated learning that reduces financial barriers.
Solution Approach 2:
The robot learns tasks autonomously by processing demonstration data through neural networks, without requiring expensive external programming services. The system serves itself by automatically generating executable programs from captured demonstrations, eliminating dependency on costly professional programming resources.
3Productivity
If significant programming time is invested, then the robot can perform complex tasks, but the ability to make changes or add new tasks is limited
Solution Approach 1:
The system enables dynamic task acquisition by allowing the robot to learn new tasks on-demand through demonstration capture and neural network processing. Rather than requiring static pre-programming, the robot can adaptively learn and modify tasks as needed, providing both complexity capability and flexibility through the same learning framework.
Solution Approach 2:
The system performs preliminary learning by capturing demonstration data and processing it through neural networks to create executable programs before actual task execution is needed. This preliminary processing enables rapid task deployment and modification without requiring extensive programming time at the point of use, enhancing both productivity and adaptability.
Data Source
AI summary
Various embodiments enable a robot, or other autonomous or semi-autonomous device or system, to receive data involving the performance of a task in the physical world. The data can be provided as input to a perception network to infer a set of percepts about the task, which can correspond to relationships between objects observed during the performance. The percepts can be provided as input to a plan generation network, which can infer a set of actions as part of a plan. Each action can correspond to one of the observed relationships. The plan can be reviewed and any corrections made, either manually or through another demonstration of the task. Once the plan is verified as correct, the plan (and any related data) can be provided as input to an execution network that can infer instructions to cause the robot, and/or another robot, to perform the task.