Personalizing Interfaces to Humans with User-Friendly Priors (2403.07192v2)

Abstract: Robots often need to convey information to human users. For example, robots can leverage visual, auditory, and haptic interfaces to display their intent or express their internal state. In some scenarios there are socially agreed upon conventions for what these signals mean: e.g., a red light indicates an autonomous car is slowing down. But as robots develop new capabilities and seek to convey more complex data, the meaning behind their signals is not always mutually understood: one user might think a flashing light indicates the autonomous car is an aggressive driver, while another user might think the same signal means the autonomous car is defensive. In this paper we enable robots to adapt their interfaces to the current user so that the human's personalized interpretation is aligned with the robot's meaning. We start with an information theoretic end-to-end approach, which automatically tunes the interface policy to optimize the correlation between human and robot. But to ensure that this learning policy is intuitive -- and to accelerate how quickly the interface adapts to the human -- we recognize that humans have priors over how interfaces should function. For instance, humans expect interface signals to be proportional and convex. Our approach biases the robot's interface towards these priors, resulting in signals that are adapted to the current user while still following social expectations. Our simulations and user study results across $15$ participants suggest that these priors improve robot-to-human communication. See videos here: https://youtu.be/Re3OLg57hp8

• The paper introduces an innovative end-to-end framework that integrates user-friendly priors to align robot signals with human expectations.
• It employs an information-theoretic approach with a KL divergence term to optimize the mutual information between human actions and robotic hidden states.
• User studies and simulations show that interfaces leveraging these priors lead to more intuitive communication and faster adaptation in complex tasks.

Accelerating Interface Adaptation with User-Friendly Priors

The paper entitled "Accelerating Interface Adaptation with User-Friendly Priors" addresses the challenge of enhancing robot-to-human communication, particularly in situations where there are no clear conventions or mutually understood signals. This work explores an approach that aligns robotic signals with human expectations by leveraging user-friendly priors, optimizing information transfer between humans and robots.

Overview

The research introduces a novel method for robot interfaces to effectively communicate complex internal states and intentions to human users, emphasizing the importance of tailoring these signals to individual interpretive nuances. The authors begin with an information-theoretic framework, aiming to maximize the correlation between human actions and the robot's hidden states over repeated interactions. The core contribution of this work is introducing intuitive priors, such as proportionality and convexity, which serve to accelerate the human's understanding and adaptation to robotic signals.

Approach and Methodology

The approach builds upon an end-to-end learning framework, previously described by the authors, that learns interface policies to maximize mutual information between human actions and the robot's hidden states. This method is enhanced by incorporating priors over the space of signal mappings, which biases the interface toward intuitive and socially expected communication patterns.

Three neural network models form the backbone of the approach, representing the robot interface, human policy, and a decoder that estimates the hidden state from observed actions and states. By integrating a Kullback-Leibler divergence term with a user-friendly prior into the optimization process, the interface can adapt its signaling strategy in real-time, resulting in better human interpretation and performance.

Results

The simulations and user studies conducted provide empirical support for the approach, demonstrating that interfaces that leverage user-friendly priors improve communication effectiveness. The results indicate significant performance improvements in tasks where humans must interpret ambiguous robotic signals, such as navigation towards hidden goals or avoiding collisions with autonomous entities. Notably, the use of convexity priors generated interfaces that users found more intuitive and consistent, aiding in faster user adaptation and improved task execution.

Implications and Future Directions

This research holds considerable implications for the development of collaborative robotic systems and human-machine interaction technologies. By explicitly accounting for typical patterns in human interpretation, robots can adjust their interfaces to facilitate better human understanding and decision-making. This approach can be crucial in fields like autonomous driving, robotic surgery, and collaborative robotics, where misinterpretation of robot signals could lead to detrimental outcomes.

Future developments may explore more sophisticated models of human priors or expand the types of signals used (e.g., auditory or haptic) to cover a broader range of application domains. Additionally, further work could assess the adaptability of these priors across diverse user groups and contexts, enhancing the universality and robustness of robotic communication interfaces.

This work opens an avenue for more seamless integration of robots into human environments, potentially increasing the efficacy and safety of human-robot collaborations across a wide array of domains. As AI continues to evolve, understanding and incorporating human expectations into robotic systems will be vital in realizing the full potential of these technologies.