Towards Senior-Robot Interaction: Reactive Robot Dog Gestures

TL;DR

This paper presents a senior-oriented quadruped robot system with intuitive gesture control and expressive social gestures, validated through simulation and real-world tests, aiming to enhance social interaction for elderly companionship.

Contribution

It introduces a gesture-based control module and a curriculum reinforcement learning approach for expressive robot dog gestures, tailored for senior social interaction.

Findings

Over 95% success rate in simulation for learned gestures

Feasibility of social gestures validated on real robot

Identified sim-to-real challenges in joint compliance and balance

Abstract

As the global population ages, many seniors face the problem of loneliness. Companion robots offer a potential solution. However, current companion robots often lack advanced functionality, while task-oriented robots are not designed for social interaction, limiting their suitability and acceptance by seniors. Our work introduces a senior-oriented system for quadruped robots that allows for more intuitive user input and provides more socially expressive output. For user input, we implemented a MediaPipe-based module for hand gesture and head movement recognition, enabling control without a remote. For output, we designed and trained robotic dog gestures using curriculum-based reinforcement learning in Isaac Gym, progressing from simple standing to three-legged balancing and leg extensions, and more. The final tests achieved over 95\% success on average in simulation, and we validated a key social gesture (the paw-lift) on a Unitree robot. Real-world tests demonstrated the feasibility and social expressiveness of this framework, while also revealing sim-to-real challenges in joint compliance, load distribution, and balance control. These contributions advance the development of practical quadruped robots as social companions for the senior and outline pathways for sim-to-real adaptation and inform future user studies.