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Introducing M: A Modular, Modifiable Social Robot

Victor Nikhil Antony, Zhili Gong, Yoonjae Kim, Chien-Ming Huang

Abstract

We present M, an open-source, low-cost social robot platform designed to reduce platform friction that slows social robotics research by making robots easier to reproduce, modify, and deploy in real-world settings. M combines a modular mechanical design, multimodal sensing, and expressive yet mechanically simple actuation architecture with a ROS2-native software package that cleanly separates perception, expression control, and data management. The platform includes a simulation environment with interface equivalence to hardware to support rapid sim-to-real transfer of interaction behaviors. We demonstrate extensibility through additional sensing/actuation modules and provide example interaction templates for storytelling and two-way conversational coaching. Finally, we report real-world use in participatory design and week-long in-home deployments, showing how M can serve as a practical foundation for longitudinal, reproducible social robotics research.

Introducing M: A Modular, Modifiable Social Robot

Abstract

We present M, an open-source, low-cost social robot platform designed to reduce platform friction that slows social robotics research by making robots easier to reproduce, modify, and deploy in real-world settings. M combines a modular mechanical design, multimodal sensing, and expressive yet mechanically simple actuation architecture with a ROS2-native software package that cleanly separates perception, expression control, and data management. The platform includes a simulation environment with interface equivalence to hardware to support rapid sim-to-real transfer of interaction behaviors. We demonstrate extensibility through additional sensing/actuation modules and provide example interaction templates for storytelling and two-way conversational coaching. Finally, we report real-world use in participatory design and week-long in-home deployments, showing how M can serve as a practical foundation for longitudinal, reproducible social robotics research.
Paper Structure (18 sections, 8 figures, 1 table)

This paper contains 18 sections, 8 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Works
  3. Long-Term, In-the-Wild Social Robotics
  4. Open-Source Robot Platforms
  5. System Overview
  6. Design Rationale
  7. Mechanical Design
  8. Software Architecture
  9. Simulation
  10. Demonstrating Usage
  11. Extensions
  12. Example Interaction: One-Way Storytelling
  13. Example Interaction: Two-Way Conversation
  14. M in the Real-World
  15. M for Child-Robot Interaction
  16. ...and 3 more sections

Figures (8)

  • Figure 1: M is an open-source, modular social robot platform designed for longitudinal, in-the-wild research. The platform features customizable embodiment (e.g., accessories, face plate), expressive behaviors (e.g., facial expressions, body gestures), multimodal sensing (e.g., touch, radar, camera), and ROS2 software supporting reproducibility, extensibility and field deployments.
  • Figure 2: Exploded view of M's mechanical design, illustrating the modular architecture. Key components include the head module (containing compute, audio, and display), head rotation and nodding modules, customizable face masks, articulated arms with magnetic attachments, camera, and body rotation module with weighted base for stability.
  • Figure 3: Illustration of M narrating a children's story with synchronized multi-modal expressive behaviors produced leveraging generative AI pipelines.
  • Figure 4: M can be programmed in a simulation environment with complete sim-to-real parity. The GUI includes a 3D viewport with orbit controls, real-time joint state displays, and interactive manipulation controls.
  • Figure 5: Illustration of M's modular sensing extensions: FMCW radar enables privacy-preserving human presence detection. Upon detecting a user (left), M initiates engagement through body reorientation and expressions (center, right), demonstrating M support for proactive engagement in privacy-sensitive deployment contexts.
  • ...and 3 more figures