Collaborative Robot Training Using Shared Demonstration Trajectories
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Solution Overview
Problem
Conventional robotic programming and training methodologies are insufficient for training multiple robots to collaborate efficiently, requiring separate training for each robot and necessitating complex simulation environments that are cumbersome and resource-intensive, and often requiring specialized technical expertise.
Innovation Solution
A method for training multiple robots simultaneously using a single ML model, utilizing a camera array to capture expert demonstrations with markers, creating a simulation environment, and processing images to identify trajectories and train a single policy model for all robots, reducing the number of required demonstrations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional robotic programming methods are used to train multiple robots, then each robot can be programmed to perform its task, but the complexity and time required increases significantly as the number of robots increases
Solution Approach 1:
The patent merges the training process of multiple robots into a single unified demonstration. Instead of programming each robot separately, a single expert demonstration captures the collaborative behavior of all robots simultaneously. The system processes the demonstration to extract trajectories for multiple robots and trains a single policy model that coordinates all robots, thereby reducing programming complexity while maintaining training efficiency.
Solution Approach 2:
The patent uses imitation learning where robots copy the behavior demonstrated by an expert. The expert demonstration is captured and processed to create reference trajectories that the robots replicate. This copying approach allows multiple robots to learn from a single demonstration without requiring separate programming for each robot, resolving the contradiction between training efficiency and programming complexity.
2Productivity
If separate training is conducted for each robot, then individual robot performance can be optimized, but the total training time and resources required increase linearly with the number of robots
Solution Approach 1:
The patent combines the training of multiple robots into a single simultaneous process. A single expert demonstration is used to train a unified policy model that controls all robots. The system processes the demonstration to extract trajectories for all robots at once and trains one policy model that coordinates their collaborative behavior, reducing total training time from linear scaling to constant time relative to the number of robots.
3Reliability
If complex simulation environments are created to train multiple robots collaboratively, then realistic collaborative behavior can be achieved, but the setup becomes cumbersome and requires specialized technical expertise
Solution Approach 1:
The patent captures real-world expert demonstrations directly and processes them to extract trajectories for training. Instead of requiring complex simulation environments, the system uses actual demonstrations from experts performing the collaborative task. The processing system extracts the necessary trajectory information and uses it to train the policy model, eliminating the need for specialized simulation setup while maintaining accurate collaborative behavior.
Solution Approach 2:
The patent replaces complex mechanical simulation environments with a computational processing system. Instead of creating physical or detailed virtual simulations of the collaborative environment, the system uses image processing and trajectory extraction from expert demonstrations. This substitution simplifies the setup process while maintaining the ability to train accurate collaborative behavior through data-driven approaches.
Data Source
AI summary
The present disclosure provides describes to train a multi policy ML model to control robots in a multi-robot system in collaborating to perform a task. For example, trajectories associated with manipulating an object to perform the collaborative task can be determined and an ML model trained to output control actions for the robots in the multi-robot system to collaborate to complete the task.