Eye-Tracking Teleoperation for Accurate Robot Intent Mapping
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Solution Overview
Problem
Existing robotic control systems face challenges in accurately interpreting user intent, leading to miscommunication and unintended actions, particularly due to user inexperience, human error, or network lag, which can result in mishandling or damage of objects in the robot's environment.
Innovation Solution
The method employs gaze detection to determine user intent by outputting a representation of the robot's environment, receiving user gaze data, and determining context based on the object within the gaze area, allowing for contextual commands to be sent to the robot, thereby improving teleoperative robotic object interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional robotic control systems are used, then the system structure is simple, but the accuracy of interpreting user intent deteriorates leading to miscommunication and unintended actions
Solution Approach 1:
The patent introduces an eye-tracking device as an intermediary between the user and the robotic control system. This mediator captures gaze data that reveals the user's intended target, which is then processed to generate corrected control commands. The intermediary resolves the contradiction by adding a specialized component that improves intent interpretation accuracy without requiring complete system redesign.
Solution Approach 2:
The patent replaces traditional mechanical control interfaces (joysticks, buttons) with a gaze-based control system. Instead of relying on physical mechanical inputs that require precise user coordination, the system uses optical eye-tracking technology to detect user intent. This substitution improves accuracy by capturing subconscious gaze direction while reducing the complexity of the physical interaction interface.
2Reliability
If traditional control interfaces are used, then the interface design is simple, but the reliability of task completion deteriorates due to user error and inexperience
Solution Approach 1:
The eye-tracking system enables self-service by automatically detecting the user's intended target through gaze direction and generating the appropriate control commands without requiring manual intervention. The system serves itself by interpreting gaze data to determine which object the user wants to manipulate, reducing reliance on user expertise and minimizing errors from improper interface usage.
Solution Approach 2:
The system implements feedback by continuously monitoring gaze data and adjusting control commands accordingly. The eye-tracking device provides real-time feedback about user attention, allowing the system to confirm or correct its interpretation of user intent before executing actions. This feedback loop significantly improves task completion reliability by preventing misinterpretation errors.
3Measurement precision
If direct gesture mapping is used, then the control response time is fast, but the accuracy of mapping gestures to correct objects deteriorates leading to unintended actions
Solution Approach 1:
The system performs preliminary action by capturing the user's gaze direction in advance of the actual control input. By determining the intended target through eye-tracking before the user completes the gesture, the system can pre-process and validate the intended action. This preliminary gaze-based target identification ensures accurate gesture-to-object mapping while maintaining fast response times through pre-computed control commands.
Data Source
AI summary
Systems and methods for determining teleoperating user intent via eye tracking are provided. A method includes outputting, to a display of a gaze-tracking device utilized by a user, a representation of an environment of a robot. The method further includes receiving, from the gaze-tracking device, user gaze data corresponding to a gaze area subject to a gaze of the user within the representation. The method also includes determining a context based upon an object within the gaze area, an object type, and characteristics of the object. The method further includes receiving, via a user interface, a user input. The method also includes outputting, to the robot, a contextual command corresponding to the context and the user input.