Robot Insertion Skills Using Motion Primitives and Bayesian Optimization
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Solution Overview
Problem
Existing robotic manipulation tasks require immense manual programming, are computationally expensive, and lack generalizability, with traditional machine learning techniques being brittle and requiring extensive retraining for minor changes.
Innovation Solution
A data-efficient framework using motion primitives and a dense objective function, leveraging human demonstrations for guidance, enables efficient learning and generalization of robotic skills through Bayesian Optimization and impedance control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional machine learning control algorithms are used for robotic manipulation, then the robot can learn tasks through data, but the computational cost becomes extremely expensive due to the complex, high-dimensional, and continuous action space
Solution Approach 1:
The patent segments the complex, high-dimensional action space into multiple low-dimensional subspaces by decomposing the manipulation task into a sequence of motion primitives. Each primitive operates in a reduced-dimensional space, dramatically lowering the computational burden of exploration and evaluation while maintaining the ability to perform complex manipulation tasks through composition of primitives.
2Adaptability or versatility
If traditional machine learning techniques are used for robotic control, then the robot can adapt to tasks, but the model becomes extremely brittle and does not generalize well to new tasks or environments
Solution Approach 1:
The patent creates a universal framework where a single set of motion primitives can perform multiple different manipulation tasks across varying environments. The primitives are designed to be task-agnostic and environment-independent, allowing the same primitive library to generalize across diverse tasks without requiring task-specific retraining, thereby achieving both adaptability and reliability.
3Extent of automation
If traditional machine learning approaches are used for robotic manipulation, then the robot can learn from data, but extensive retraining and data collection are required even for tiny changes to the task, robot, or environment
Solution Approach 1:
The patent performs preliminary action by pre-defining a library of motion primitives that capture fundamental manipulation patterns. This preliminary structuring of the action space allows the system to adapt to new tasks by composing existing primitives rather than learning from scratch, dramatically reducing retraining time and data requirements when tasks or environments change.
Data Source
AI summary
A computer-implemented method comprising, receiving data representing a successful trajectory for an insertion task using a robot to insert a connector into a receptacle, performing a parameter optimization process for the robot to perform the insertion task. This parameter optimization includes defining an objective function that measures a similarity of a current trajectory generated with a current set of parameters to the successful trajectory and repeatedly modifying the current set of parameters and evaluating the modified set of parameters according to the objective function until generating a final set of parameters.