# Safe and Efficient Exploration of Human Models During Human-Robot Interaction

arXiv: https://arxiv.org/abs/2208.01103 · HTML: https://mlsanity.com/paper/2208.01103/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Aug 2022.

## Built on by (most cited first, 3 of 3)

- [Safe Reinforcement Learning of Dynamic High-Dimensional Robotic Tasks: Navigation, Manipulation, Interaction](https://mlsanity.com/paper/2209.13308/) (2022)
- [Safe Reinforcement Learning on the Constraint Manifold: Theory and Applications](https://mlsanity.com/paper/2404.09080/) (2024)
- [PRISM: Predictive Representation of Interaction Style and Motion for Social Robot Navigation](https://mlsanity.com/paper/2609.18125/) (2026)

## Abstract (arXiv)

Many collaborative human-robot tasks require the robot to stay safe and work efficiently around humans. Since the robot can only stay safe with respect to its own model of the human, we want the robot to learn a good model of the human in order to act both safely and efficiently. This paper studies methods that enable a robot to safely explore the space of a human-robot system to improve the robot's model of the human, which will consequently allow the robot to access a larger state space and better work with the human. In particular, we introduce active exploration under the framework of energy-function based safe control, investigate the effect of different active exploration strategies, and finally analyze the effect of safe active exploration on both analytical and neural network human models.