Robot Autonomy Control Using Tele-Operation Feedback
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Solution Overview
Problem
Existing semi-autonomous robots often require significant human intervention to make decisions in uncertain or dynamic environments, limiting their autonomy and efficiency.
Innovation Solution
A method and system that enables semi-autonomous robots to identify candidate actions, collect ancillary data, and request instructions from a tele-operation system to update a control model, thereby increasing their autonomy by allowing them to make more informed decisions and reduce reliance on human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If semi-autonomous robots require significant human intervention to make decisions, then the robot operation is reliable and controllable, but the robot autonomy and efficiency are limited
Solution Approach 1:
The decision-making process is segmented into distinct components: a candidate action generator that proposes multiple possible actions, a scorer that evaluates each candidate, and a selector that chooses the best action. This segmentation allows the robot to systematically break down complex decision-making into manageable steps, improving autonomy while maintaining controllable complexity through modular architecture.
Solution Approach 2:
The system performs preliminary actions by pre-generating multiple candidate actions before making a final decision. Instead of directly selecting one action, the robot first creates a set of potential actions, scores them, and then selects the best one. This preliminary generation and evaluation process enables more informed decision-making and increases robot autonomy.
2Productivity
If the robot operates with high autonomy to reduce human intervention, then the efficiency and productivity improve, but the reliability and control become challenging
Solution Approach 1:
The system incorporates feedback mechanisms where the scorer evaluates candidate actions based on learned patterns and previous experiences. The control model is updated based on the selected actions and their outcomes, creating a feedback loop that continuously improves decision-making reliability. This feedback-driven approach allows the robot to maintain high autonomy while improving reliability over time through learning from experience.
Solution Approach 2:
The robot performs self-service by autonomously generating, evaluating, and selecting its own actions without continuous human intervention. The control model serves itself by using accumulated experience to improve future decisions. This self-service capability increases productivity and operational efficiency while maintaining reliability through the robot's own learned decision-making processes.
3Measurement precision
If the robot collects and processes extensive ancillary data for each candidate action, then the decision-making quality improves, but the computational load and time consumption increase
Solution Approach 1:
The system applies partial action by collecting and processing only the most relevant ancillary data needed for scoring candidate actions, rather than analyzing every possible data point. The scorer prioritizes key features and data elements that have the greatest impact on decision quality, achieving sufficient measurement precision without excessive data processing time.
Solution Approach 2:
The system changes parameters by dynamically adjusting which ancillary data are collected and how deeply they are processed based on the situation. The control model learns to identify critical parameters and data elements that matter most for specific decision contexts, allowing the robot to maintain high decision-making precision while reducing overall computational load and time consumption by focusing on key parameters rather than processing all data equally.
Data Source
AI summary
In a method of operation of a robot, the robot identifies a set of candidate actions that may be performed by the robot, and collects, for each candidate action of the set of candidate actions, a respective set of ancillary data. The robot transmits a request for instructions to a tele-operation system that is communicatively coupled to the robot. The request for instructions includes each candidate action and each respective set of ancillary data. The robot receives, and executes, the instructions from the tele-operation system. The robot updates a control model, based at least in part on each candidate action, each respective set of ancillary data, and the instructions, to increase a level of autonomy of the robot. The robot may transmit the request for instructions to the tele-operation system in response to determining the robot is unable to select a candidate action to perform in furtherance of an objective.