# Leading or Following? Dyadic Robot Imitative Interaction Using the Active Inference Framework

arXiv: https://arxiv.org/abs/2103.02137 · HTML: https://mlsanity.com/paper/2103.02137/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 29 references that are in the map.
- First posted on arXiv 3 Mar 2021.

## Builds on (most cited first, 5 of 5)

- [A Recurrent Latent Variable Model for Sequential Data](https://mlsanity.com/paper/1506.02216/) (2015)
- [Reservoir computing approaches for representation and classification of multivariate time series](https://mlsanity.com/paper/1803.07870/) (2018)
- [Adaptive robot body learning and estimation through predictive coding](https://mlsanity.com/paper/1805.03104/) (2018)
- [Cognitive and motor compliance in intentional human-robot interaction](https://mlsanity.com/paper/1911.01753/) (2019)
- [Tracking Emotions: Intrinsic Motivation Grounded on Multi-Level Prediction Error Dynamics](https://mlsanity.com/paper/2007.14632/) (2020)

## Built on by (most cited first, 5 of 5)

- [Active Inference in Robotics and Artificial Agents: Survey and Challenges](https://mlsanity.com/paper/2112.01871/) (2021)
- [pymdp: A Python library for active inference in discrete state spaces](https://mlsanity.com/paper/2201.03904/) (2022)
- [Branching Time Active Inference: empirical study and complexity class analysis](https://mlsanity.com/paper/2111.11276/) (2021)
- [Interactive inference: a multi-agent model of cooperative joint actions](https://mlsanity.com/paper/2210.13113/) (2022)
- [Seeing Through Uncertainty: Free-Energy-Inspired Real-Time Adaptation for Robust Visual Navigation](https://mlsanity.com/paper/2403.01977/) (2024)

## Abstract (arXiv)

This study investigated how social interaction among robotic agents changes dynamically depending on the individual belief of action intention. In a set of simulation studies, we examine dyadic imitative interactions of robots using a variational recurrent neural network model. The model is based on the free energy principle such that a pair of interacting robots find themselves in a loop, attempting to predict and infer each other's actions using active inference. We examined how regulating the complexity term to minimize free energy determines the dynamic characteristics of networks and interactions. When one robot trained with tighter regulation and another trained with looser regulation interact, the latter tends to lead the interaction by exerting stronger action intention, while the former tends to follow by adapting to its observations. The study confirms that the dyadic imitative interaction becomes successful by achieving a high synchronization rate when a leader and a follower are determined by developing action intentions with strong belief and weak belief, respectively.