Robotic Intent Inference for Stable Operator-Guided Control
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Solution Overview
Problem
Conventional robotic systems face challenges in accurately mimicking human actions, leading to unstable performance, and intent-based action generation is limited to video games, lacking practical applications in controlling robotic devices based on inferred operator intent.
Innovation Solution
An intent-based system that monitors an operator's actions, infers the intended target and action, and controls the robotic device to perform the intended action, using a combination of gaze tracking, gesture tracking, and semantic task parsing to guide the robotic device in environments like kitchens or autonomous vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional robotic systems use traditional tracking methods to mimic human actions, then the system can perform basic tracking functions, but the performance becomes unstable and inaccurate
Solution Approach 1:
The patent introduces an intent-based intermediary system that sits between the operator's actions and the robotic device control. This intermediary infers the operator's intended target and action before executing commands, acting as a mediator that stabilizes and refines the control signal. The system uses gaze tracking, gesture tracking, and semantic task parsing as intermediate processing layers to translate raw operator inputs into stable, accurate robotic actions.
Solution Approach 2:
The system performs preliminary inference of the operator's intent before executing the actual control action. By predicting the intended target and action in advance through semantic task parsing and pattern recognition, the system prepares the optimal control command beforehand, ensuring accurate and stable execution even when raw tracking data has noise or ambiguity.
2Ease of operation
If intent-based control systems are implemented in robotic devices, then the operator's intent can be accurately inferred and executed, but the system complexity increases significantly
Solution Approach 1:
The patent segments the intent-based control system into distinct functional modules: gaze tracking module, gesture tracking module, semantic task parsing module, and intent inference module. Each module handles a specific aspect of intent recognition independently, making the overall complex system manageable through modular design. This segmentation allows each component to be optimized separately while maintaining overall system functionality.
Solution Approach 2:
The system employs universal algorithms for intent inference that can be applied across different robotic tasks and environments. The semantic task parsing and pattern recognition mechanisms are designed to be task-agnostic, enabling the same core infrastructure to handle diverse operations from simple tracking to complex manipulation, thereby reducing the need for task-specific complexity.
3Measurement precision
If multiple tracking methods (gaze tracking, gesture tracking) are combined to improve intent inference, then the accuracy of target identification improves, but the computational requirements and processing time increase
Solution Approach 1:
The system implements periodic updates of intent inference rather than continuous full-processing. It periodically fuses data from gaze tracking, gesture tracking, and semantic parsing at key decision points, reducing the computational burden while maintaining high accuracy. The system updates the intent model at appropriate intervals based on task progress and operator behavior patterns, rather than continuously processing all inputs at maximum resolution.
Data Source
AI summary
A method performed by an autonomous device includes identifying a current movement of an operator based on monitoring the operator of the autonomous device. The method also includes inferring an intended direction of travel for the autonomous device based identifying the current movement. The method further includes identifying one or more objects in a current environment and limitations of the current environment. The method still further includes determining the action to be performed based on inferring the intended direction of travel and also identifying the one or more objects and the limitations of the current environment. The method also includes performing, by the autonomous device, the action.