Robotic Teleoperation Intention Estimation via Gaze and Motion Analysis
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Solution Overview
Problem
Robotic teleoperation faces challenges such as communication delays, inaccurate positioning, and differences between human operators' intentions and remote robotic actions due to noise and variability in real-world conditions, leading to inefficiencies and potential failures in tasks like warehouse management, medical surgeries, and hazardous environments.
Innovation Solution
A system for robotic teleoperation intention estimation that uses a neural network to analyze motion and gaze data from human operators, generating latent actions and temporally associated inferences, and a dynamic transition model to infer intentions with probabilistic uncertainty, allowing robotic elements to perform tasks autonomously without direct control signals, using contrastive learning and hierarchical clustering to refine the model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If human operators directly control robotic elements in real-time, then task execution accuracy is maintained, but communication delays and operator workload reduce productivity
Solution Approach 1:
The system performs preliminary actions by training the neural network and transition model offline with大量 teleoperation data before actual use. The model learns to predict robotic element actions and human intentions in advance, enabling autonomous execution without real-time operator intervention for routine tasks, thus improving productivity while maintaining reliability through pre-learned accuracy
Solution Approach 2:
The robotic system serves itself by using the trained neural network to autonomously predict and execute actions based on human intention estimation. The system self-corrects positioning errors and adapts to environmental changes without continuous operator input, reducing communication delays impact while maintaining task accuracy through self-learning capabilities
2Measurement precision
If the system uses complex neural networks and dynamic transition models to estimate human intentions, then task execution accuracy improves, but system complexity and computational requirements increase
Solution Approach 1:
The system segments the intention estimation problem into distinct components: a neural network for extracting features from motion and gaze data, a transition model for predicting action sequences, and an intention hierarchy for organizing high-level goals. This modular segmentation allows each component to be optimized independently while maintaining overall accuracy, reducing the burden on any single system element
Solution Approach 2:
The neural network and transition model are trained offline with large datasets before deployment. This preliminary training phase pre-computes the complex relationships between human inputs and robotic actions, so that during actual teleoperation, the system only needs to query the pre-trained models, significantly reducing real-time computational complexity while maintaining high estimation accuracy
3Ease of operation
If the robotic system autonomously performs tasks based on inferred intentions, then operator workload is reduced, but reliability decreases due to potential misinterpretation of human intentions
Solution Approach 1:
The system incorporates feedback mechanisms where the predicted robotic actions and estimated intentions are continuously monitored and compared with actual operator inputs and environmental outcomes. The neural network and transition model are refined using this feedback through contrastive learning and Bayesian hierarchical modeling, improving intention interpretation accuracy over time while maintaining low operator workload through autonomous execution
Solution Approach 2:
The system dynamically adjusts the level of autonomy based on task criticality and confidence in intention estimation. For high-stakes tasks or uncertain predictions, the system requests clarification from the operator, while for routine tasks with high confidence, it executes autonomously. This dynamic adjustment maintains reliability by involving operators when needed while reducing workload through autonomous handling of predictable tasks
Data Source
AI summary
Systems and methods for robotic teleoperation are provided. The system receives motion input and gaze data of a human operator performing a robotic teleoperation task, and a plurality of features are extracted from the received data. A recurrent neural network determines latent actions from the extracted features and generates temporally associated action inferences. A model determines inferred intention from a sequenced pair of action inferences, an intention hierarchy, and a probabilistic uncertainty function. A teleoperated robotic element performs a task associated with the inferred intention without a corresponding control signal from the human operator. The recurrent neural network is trained using extracted features and contrastive learning. The intention hierarchy is generated from action inferences. The probabilistic uncertainty function is generated using Bayesian hierarchical modeling. The system and method improves robotic teleoperation by estimating the intention of the human operator.