Autonomous Skill Composition With Learned Sub-Task Termination
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Machine learning models face challenges in determining the completion of sub-tasks in long-running tasks, such as robotic operations, leading to inefficiencies and potential destruction, as existing methods lack a clear mathematical formula or sufficient feedback to ensure task completion.
Innovation Solution
A machine learning model is augmented to learn a termination condition for sub-tasks, using reinforcement learning and synthetic scenarios to provide a termination signal, allowing subsequent sub-tasks to begin seamlessly by training on positive reinforcement when sub-task completion is achieved.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional machine learning techniques are used to perform sub-tasks, then the model can infer positions and angles, but the model cannot determine when the sub-task is complete, leading to trial and error
Solution Approach 1:
The patent introduces a termination condition that provides feedback to the machine learning model about whether a sub-task has been completed. This feedback mechanism allows the model to know when to transition from one sub-task to the next, eliminating the need for trial and error and improving both reliability of completion determination and productivity by preventing redundant actions.
Solution Approach 2:
The patent segments the overall task into distinct sub-tasks with clearly defined termination conditions for each. By dividing the long-horizon task into manageable segments with explicit completion criteria, the system can reliably determine when each segment is finished and proceed to the next, improving both completion determination and overall task efficiency.
2Reliability
If the robotic arm performs trial and error to determine grasp strength, then it may eventually succeed, but the process becomes inefficient and potentially destructive
Solution Approach 1:
The patent applies preliminary action by establishing termination conditions before executing the grasp sub-task. The model is trained to predict when a grasp will be successful based on learned patterns from training data, allowing it to plan the appropriate grasp force and duration in advance rather than relying on trial and error, thereby preventing object damage while maintaining high success rates.
Solution Approach 2:
The termination condition provides feedback about grasp success, allowing the model to learn from both successful and failed attempts during training. This feedback mechanism enables the model to improve its grasp predictions without requiring additional trial and error during actual task execution, protecting objects from damage while maintaining reliability.
3Adaptability or versatility
If no mathematical formula exists to determine sub-task completion, then traditional control methods fail, but machine learning can learn termination conditions from data
Solution Approach 1:
The patent replaces traditional mechanical control systems with explicit termination conditions with a machine learning-based system. Instead of using complex mathematical formulas or sensors to detect completion, the ML model learns termination conditions directly from training data, substituting the mechanical/sensor-based approach with a data-driven approach that handles complex, non-linear termination criteria more effectively.
Solution Approach 2:
The patent changes the parameter representation by using continuous probability outputs from the ML model rather than discrete binary states. The termination condition is represented as a probability value that can be thresholded, allowing for nuanced decision-making about sub-task completion while maintaining computational simplicity during execution despite the complexity during training.
Data Source
AI summary
The techniques disclosed herein enable a machine learning model to learn a termination condition of a sub-task. A sub-task is one of a number of sub-tasks that, when performed in sequence, accomplish a long-running task. A machine learning model used to perform the sub-task is augmented to also provide a termination signal. The termination signal indicates whether the sub-task's termination condition has been met. Monitoring the termination signal while performing the sub-task enables subsequent sub-tasks to seamlessly begin at the appropriate time. A termination condition may be learned from the same data used to train other model outputs. In some configurations, the model learns whether a sub-task is complete by periodically attempting subsequent sub-tasks. If a subsequent sub-task can be performed, positive reinforcement is provided for the termination condition. The termination condition may also be trained using synthetic scenarios designed to test when the termination condition has been met.