# Towards Adaptive Interaction Strategies for Human Companion Robot via Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2609.25031 · HTML: https://mlsanity.com/paper/2609.25031/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 10 of its 57 references that are in the map.
- First posted on arXiv 20 Aug 2026.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 10 of 10)

- [Information Theoretic Model Predictive Control: Theory and Applications to Autonomous Driving](https://mlsanity.com/paper/1707.02342/) (2017)
- [Safety-Critical Model Predictive Control with Discrete-Time Control Barrier Function](https://mlsanity.com/paper/2007.11718/) (2020)
- [skrl: Modular and Flexible Library for Reinforcement Learning](https://mlsanity.com/paper/2202.03825/) (2022)
- [Shield Model Predictive Path Integral: A Computationally Efficient Robust MPC Approach Using Control Barrier Functions](https://mlsanity.com/paper/2302.11719/) (2023)
- [Person Following by Autonomous Robots: A Categorical Overview](https://mlsanity.com/paper/1803.08202/) (2018)
- [LBGP: Learning Based Goal Planning for Autonomous Following in Front](https://mlsanity.com/paper/2011.03125/) (2020)
- [An MCTS-DRL Based Obstacle and Occlusion Avoidance Methodology in Robotic Follow-Ahead Applications](https://mlsanity.com/paper/2309.16884/) (2023)
- [RPF-Search: Field-based Search for Robot Person Following in Unknown Dynamic Environments](https://mlsanity.com/paper/2503.02188/) (2025)
- [Human-Following and -guiding in Crowded Environments using Semantic Deep-Reinforcement-Learning for Mobile Service Robots](https://mlsanity.com/paper/2206.05771/) (2022)
- [Adaptive Companionship for Group-Following Robots: Handling Dynamically Changing Group Formations](https://mlsanity.com/paper/2607.01287/) (2026)

## Abstract (arXiv)

In the field of Human-Robot Interaction (HRI), achieving flexibility in human-accompanying within real-world environments holds great potential for various applications but also poses significant challenges. Traditional methods typically restrict robots to fixed positions relative to humans, such as tracking from behind, in front, or side-by-side, which limits robot adaptability in dynamic workspaces. This study introduces a novel human-companioning strategy that uses Reinforcement Learning (DRL) to enable mobile robots to dynamically adjust their tracking positions according to varying conditions. An interaction space is defined to capture the relationship between the human and the robot while considering the environment, which serves as the basis for state spaces in DRL to assist the robot in adapting to environmental changes. A human-robot companion controller is developed by integrating Model Predictive Path Integral (MPPI) control with Control Barrier Functions (CBF), ensuring that the robot accurately follows the target's movement in both position and orientation while avoiding obstacles and enhancing social acceptance and safety. The proposed approach is evaluated in real-world scenarios, both indoors and outdoors, and compared with other studies. The results show that the proposed method improves the success rate and tracking accuracy by at least 24% and 47%, respectively, while enhancing human comfort. Experiments demonstrate the robot's ability to flexibly accompany a person walking at speeds of up to 1.7 m/s, dynamically adjusting its strategy without being confined to a fixed position. Additionally, the robot respects the human's intimate space to ensure safety, comfort, and effective obstacle avoidance.