Robot Task Learning From Demonstration for Flexible Production
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Solution Overview
Problem
Current robotic machine interfaces require skilled programmers to define tasks, communicate with robots, and optimize operations, which is time-consuming and costly, especially in environments with high product variations and short production cycles.
Innovation Solution
A method for robotic machine task learning through demonstration using a universal controller that captures data from spatial sensors, analyzes human or robotic demonstrations, and translates the data into robotic machine instructions, allowing for intuitive gesture-based communication and reduced risk of collisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If skilled programmers are used to define tasks and optimize robotic machine operations, then task definition precision and optimization quality are improved, but project costs and time consumption increase significantly
Solution Approach 1:
The robotic machine performs self-learning by automatically observing demonstrations, recording sensor data, representing movements in task space, and selecting subsets for imitation learning without human programmer intervention. This self-service capability eliminates the need for skilled programmers to manually define tasks, thereby reducing both project costs and time consumption while maintaining task definition precision
Solution Approach 2:
The patent replaces the mechanical process of manual programming with an automated learning system. Instead of programmers manually coding task definitions and optimizing operations, the system uses sensorial data streams, task space representations, and imitation learning algorithms to automatically acquire and refine task knowledge, substituting human expertise with an autonomous learning mechanism
2Reliability
If skilled programmers are used to coordinate tasks between multiple robotic machines, then coordination quality and team performance are improved, but operational costs increase
Solution Approach 1:
Each robotic machine in the team autonomously learns task coordination through demonstration observation and imitation learning. The machines automatically record sensor data, process task space representations, and select appropriate movement subsets for coordination tasks without requiring skilled programmers to manually configure inter-machine communication and coordination protocols, thereby maintaining coordination quality while reducing operational costs
3Manufacturing precision
If traditional programming methods are used to adapt robotic machines to high product variations, then task accuracy is maintained, but production cycle time increases
Solution Approach 1:
The system dynamically adapts to product variations through continuous learning from demonstrations. Instead of static programming that requires reconfiguration for each product variation, the robotic machines use sensorial data streams and task space representations to learn new movements and coordinate tasks adaptively in real-time, maintaining task accuracy while significantly reducing production cycle times for high-variation environments
Data Source
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AI summary
A method for defining a robotic machine task. The method comprises a) collecting a sequence of a plurality of images showing at least one demonstrator performing at least one manipulation of at least one object, b) performing an analysis of said sequence of a plurality of images to identify demonstrator body parts manipulating said at least one object and said at least one manipulation of said at least one object, c) determining at least one robotic machine movement to perform said task, and d) generating at least one motion command for instructing said robotic machine to perform said task.