Robotic Planning with Human Latent-State Belief Modeling
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Solution Overview
Problem
Robotic agents deployed in environments with human agents face challenges in planning due to uncertain and irrational human behaviors, as existing models either exploit homogeneous cognitive models or passively adapt, failing to actively learn and account for heterogeneous cognitive states.
Innovation Solution
A system integrating iterative reasoning models, partially observable Markov decision processes (POMDP), and Monte-Carlo belief tree search to model human intelligence and rationality, allowing robotic agents to actively learn and exploit human cognitive limitations for improved planning and interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing cognitive models are used to plan robotic agent behavior, then planning can be performed, but the models fail to account for heterogeneous and irrational human cognitive states
Solution Approach 1:
The system dynamically adapts the cognitive model parameters based on observed human behavior. Instead of using fixed homogeneous cognitive models, the system continuously updates beliefs about human latent states (cognitive parameters) as new observations are made, allowing the model to evolve and adapt to heterogeneous human cognitive characteristics during interaction
Solution Approach 2:
The system changes the parameters of the cognitive model to reflect different human cognitive states. By maintaining a belief distribution over possible human cognitive parameters and updating these parameters based on observations, the system can account for variability in human intelligence, rationality, and cognitive limitations across different individuals and situations
2Productivity
If robotic agents passively adapt to human behavior, then some level of interaction is achieved, but active learning and exploitation of human cognitive limitations is not possible
Solution Approach 1:
The system uses feedback from human observations to actively learn about human cognitive states. By observing human responses to robotic actions and using this feedback to update beliefs about human latent states, the system can actively infer cognitive parameters rather than merely passively adapting, thereby reducing information loss about human cognitive characteristics
Solution Approach 2:
The system performs preliminary actions designed to probe and reveal human cognitive states. By strategically selecting actions that will maximize information gain about human latent states (such as actions that will reveal human intelligence level or rationality), the system actively gathers information before making final planning decisions
3Device complexity
If homogeneous cognitive models are used, then model complexity is reduced, but heterogeneous human cognitive states cannot be accurately represented
Solution Approach 1:
The system applies local quality by allowing different cognitive parameters to have different values for different human agents or different situations. Instead of using a single homogeneous cognitive model for all humans, the system maintains separate belief distributions for different cognitive parameters (intelligence, rationality, etc.) that can be independently estimated and updated based on specific observations
Data Source
AI summary
Systems and methods for incorporating latent states into robotic planning are provided. In one embodiment, the method includes identifying an agent team including at least one robotic agent and at least one human agent. The method also includes receiving sensor data associated with relative physical parameters between the at least one robotic agent and the at least one human agent. The method further includes modeling the latent states of the at least one human agent as a behavior model. The latent states describe cognition of the at least one human agent. The method includes calculating a first belief state based on the relative physical parameters and the behavior model. The method yet further includes predicting future probabilities of future observations at a second the future probabilities.