Human-Robot Collaboration Intent Prediction via Probabilistic Modeling
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Solution Overview
Problem
Current human-robot collaboration systems lack efficient and generic intent estimation mechanisms, limiting their ability to perform complex tasks safely and efficiently, especially in environments with short observation windows, such as autonomous driving and factory settings, where accurate prediction of human actions is crucial for safe and efficient operation.
Innovation Solution
A human-robot collaboration system that incorporates a crowdsourced task description subsystem, manipulation and moving path intent prediction subsystem, and intent and task-aware motion planning subsystem, using probabilistic models and generative algorithms to predict human intent and adapt motion planning accordingly, enabling more effective and safe collaboration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If robots use short observation windows for real-time operation, then response speed is improved, but prediction accuracy of human actions deteriorates
Solution Approach 1:
The system performs preliminary action by pre-collecting and storing contextual information about human behaviors, environments, and task contexts in databases before real-time operation. This allows the robot to quickly query and utilize pre-processed information during short observation windows, maintaining both fast response and accurate prediction.
Solution Approach 2:
The system introduces contextual information databases as an intermediary between the robot's short observation window and the human action prediction task. This intermediary layer stores and organizes relevant contextual data, enabling the robot to augment limited real-time observations with rich pre-collected context, thereby improving prediction accuracy without sacrificing response speed.
2Reliability
If robots perform basic tasks with little autonomy, then safety is improved, but collaboration efficiency deteriorates
Solution Approach 1:
The system implements dynamic autonomy adjustment where the robot's level of autonomy is not fixed but adapts based on task requirements, environmental context, and collaboration needs. The robot can operate with higher autonomy in safe, predictable contexts while maintaining lower autonomy in more critical situations, thus achieving both safety and collaboration efficiency.
Solution Approach 2:
The system changes the parameter of autonomy level dynamically based on contextual information. By analyzing task complexity, environmental factors, and human-robot interaction context, the robot adjusts its autonomy parameter to optimize the balance between safety and collaboration efficiency for different operational scenarios.
3Device complexity
If robots lack contextual information about human behavior, then system complexity is reduced, but intent estimation accuracy deteriorates
Solution Approach 1:
The system segments the complexity by dividing contextual information processing into separate modules: context collection, context storage, and context querying. This segmentation allows the robot to access necessary contextual information without having to process all contextual data in real-time, maintaining low system complexity while improving intent estimation accuracy.
4Device complexity
If robots operate without generic intent estimation mechanisms, then device complexity is reduced, but task flexibility deteriorates
Solution Approach 1:
The system implements a universal intent estimation mechanism that can handle multiple task types and human behaviors through a single framework. By using probabilistic models and contextual information databases that store diverse human behavior patterns, the robot can estimate intent across different tasks (assembly, inspection, material handling) without requiring task-specific mechanisms, thus achieving task flexibility with moderate complexity.
Data Source
AI summary
A human-robot collaboration system, including at least one processor; and a non-transitory computer-readable storage medium including instructions that, when executed by the at least one processor, cause the at least one processor to: predict a human atomic action based on a probability density function of possible human atomic actions for performing a predefined task; and plan a motion of the robot based on the predicted human atomic action.