Collaborative Robot Controller Training for Interdependent Tasks
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Solution Overview
Problem
Current methods for training autonomous machines in groups are complex and limited in scalability due to the need for independent training of each robot, which fails to account for interdependencies between robots, leading to increased likelihood of system failure and requiring human intervention.
Innovation Solution
Implementing deep reinforced learning across robot controllers to enable self-training and account for interdependencies, allowing robots to autonomously adapt and perform collaborative tasks with reduced errors and increased efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If independent training of each robot is performed, then training can be done separately for each robot, but training complexity increases and scalability is limited
Solution Approach 1:
The patent merges the training processes of multiple robots into a unified deep reinforcement learning system. Multiple robot controllers are trained simultaneously using shared experience data collected from all robots performing collaborative tasks, allowing the system to learn interdependencies between robots during training rather than requiring separate independent training for each robot.
Solution Approach 2:
The patent creates a universal training system where a single deep reinforcement learning framework can train multiple different robot controllers simultaneously. The experience data collected from various robots performing different sub-tasks can be used to train all controllers, making the training system multi-functional and applicable to diverse robotic configurations.
2Ease of manufacture
If independent training of each robot is performed, then each robot can be trained separately, but interdependencies between robots are not accounted for leading to system failure
Solution Approach 1:
The patent implements feedback mechanisms where experience data collected from actual collaborative task performance is used to continuously update and improve the robot controllers during training. The system observes interactions between robots during training episodes and uses this feedback to adjust policies, ensuring that interdependencies are properly learned and system reliability is improved.
Solution Approach 2:
The patent performs preliminary training of multiple robot controllers together in a simulated environment before actual deployment. By pre-training the controllers to understand their interdependencies through repeated collaborative task episodes, the system prepares the robots to work together reliably from the start, preventing system failures that would occur with independent training.
3Ease of operation
If traditional training methods are used, then training can be performed with simple setups, but human intervention is required and scalability is limited
Solution Approach 1:
The patent enables the robotic system to train itself autonomously through self-supervised learning. The robots collect their own experience data during collaborative task performance and use this data to automatically update their controllers without requiring external human intervention for data collection or model training, making the system self-sufficient and scalable.
Solution Approach 2:
The patent creates a dynamic training system where the training process adapts automatically as more robots are added to the system. The deep reinforcement learning framework dynamically adjusts to new configurations and continuously learns from ongoing operational data, allowing the system to scale seamlessly without requiring reconfiguration or additional human intervention.
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AI summary
Methods and apparatus to train interdependent autonomous machines are disclosed. An example method includes performing an action of a first sub-task of a collaborative task with a first collaborative robot in a robotic cell while a second collaborative robot operates in the robotic cell according to a first recorded action of the second collaborative robot, the first recorded action of the second collaborative robot recorded while a second robot controller associated with the second collaborative robot is trained to control the second collaborative robot to perform a second sub-task of the collaborative task, and training a first robot controller associated with the first collaborative robot based at least on a sensing of an interaction of the first collaborative robot with the second collaborative robot while the action of the first sub-task is performed by the first collaborative robot and the second collaborative robot operates according to the first recorded action.