Mutual Adaptation and Influence: Survey of Latent Dynamics Models in Human-Robot Interaction
M.S. Smith, Sunny Amatya, Seyed Yousef Soltanian, Jonathan Bush, Wenlong Zhang · 2025
Recent advances in robotics have enabled more dynamic and interactive roles when working with humans. This is partially afforded by the use of latent states, which allow the robot to predict human actions in a compact and tractable way. This paradigm has allowed robots not only to respond and adapt to human behavior but also to anticipate and plan proactively through the use of latent dynamics models. This survey explores the use of such models in the field of human-robot interaction (HRI). Current works revealed three classes of latent dynamics: Bayesian, Markovian, and encoded. To connect these works, we synthesize a unified framework consisting of a prediction and control step. This framework shows how the traditional oneway adaptation extends to mutually adaptive behaviors by using latent dynamics in the prediction step. Similarly, we show how influence is an extension of mutual adaptation by using latent dynamics in the control step. We then review state-of-the-art approaches to mutual adaptation and influence for the three latent dynamics classes and discuss emergent properties as they relate to application, update frequency, and specific considerations for mutual adaptation or influence. Using this review, we highlight gaps in the current literature and propose future directions.