Proactive Robot Planning with Multi-Agent Human Intent Simulation
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Solution Overview
Problem
Current robots are largely reactive and lack the ability to proactively understand and meet the intentions of multiple human agents with competing interests, often requiring explicit instructions and struggling with generalization beyond training data, leading to limited usefulness in shared environments.
Innovation Solution
A robot system that utilizes multi-agent simulation and large language models to explore behavior options, assess human reactions, and update profiles based on feedback, enabling proactive behavior planning that considers human preferences and emotions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If robots use frequentist approach for behavior planning based on statistical analysis of past data, then they can predict future behavior patterns, but they require large amounts of training data and lack generalization ability beyond training data
Solution Approach 1:
The system performs preliminary multi-agent simulation to predict human reactions before executing robot behaviors. By simulating various scenarios in advance and evaluating satisfaction ratings, the robot can proactively plan behaviors that are likely to satisfy human intentions without requiring extensive real-world training data.
Solution Approach 2:
The patent introduces a multi-agent simulation environment as an intermediary between the robot and real human interactions. This virtual environment allows the robot to test and evaluate behavior options against simulated human agents with predefined profiles, enabling generalization to new situations without direct real-world trial and error.
2Measurement precision
If robots rely on explicit commands from humans for behavior planning, then they can accurately follow user intentions, but they lack proactive ability to anticipate and meet user needs
Solution Approach 1:
The system generates multiple candidate behaviors and simulates their outcomes before execution. By proactively predicting human reactions and evaluating satisfaction ratings through multi-agent simulation, the robot can anticipate user needs and select behaviors that align with human intentions without waiting for explicit commands.
Solution Approach 2:
The patent implements a feedback mechanism where the robot evaluates simulated human reactions to its behavior options. Based on the satisfaction ratings from simulated human agents, the robot can refine its behavior selection and improve its ability to anticipate and meet actual user needs over time.
3Ease of operation
If robots use large language models for behavior planning, then they can generate natural language responses, but they struggle with exhaustive situation analysis and decision-making when multiple human agents have competing interests
Solution Approach 1:
The system segments the complex decision-making process into distinct components: generating candidate behaviors, simulating each option with multi-agent models, evaluating satisfaction ratings, and selecting the optimal behavior. This segmentation allows the robot to handle multiple human agents with competing interests systematically rather than attempting to analyze all possibilities simultaneously.
Solution Approach 2:
The multi-agent simulation environment serves as an intermediary that handles the complex analysis of situations involving multiple human agents. Instead of the LLM directly analyzing competing interests, the simulation models predict reactions and generate satisfaction ratings, simplifying the decision-making process while maintaining natural language interaction capabilities.
4Adaptability or versatility
If robots operate in noisy environments or with impaired communication, then they can function in challenging conditions, but they fail to choose appropriate behaviors due to lack of guidance
Solution Approach 1:
The system performs preliminary simulation of multiple behavior options before execution, allowing the robot to evaluate potential outcomes even when explicit human guidance is unavailable. By pre-evaluating satisfaction ratings through multi-agent simulation, the robot can make reliable behavior selections in noisy environments or when human communication is impaired.
Data Source
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AI summary
A robot system for executing at least one behavior in an interaction with at least one human agent in an environment comprising a robot configured to execute the at least one behavior, at least one sensor configured to obtain information on the environment, and a processing means configured to detect the at least one human agent based on the obtained information. The system comprises an interface for accessing a common-sense inference engine in the form of a large language model. The processing means is configured to predict a tree of behaviors of a human-robot interaction for performing the task by prompting the large language model a plurality of times and to assign cost to each behavior of the robot in the predicted tree of behaviors. The assigned costs include behavior cost for the robot and target achievement satisfaction cost of the at least one human agent in reaction to the behavior of the robot. In addition, the processing means selects a sequence of behaviors from the predicted tree of behaviors based on the assigned cost and controls at least one actuator of the robot based on the selected sequence of behaviors.