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.
Figures & tables
Fig. 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.
Robot
Cost
Modular
Multi-Sensor
Expressivity
On-Board Compute
Sim2Real
Field Readiness
Blossom [ 9 ]
$250
∘
∘
4 DoF
-
-
∘
Poppy-H [ 10 ]
$8-10K
∙
∙
25 DoF, LCD
-
∙
-
Ono [ 11 ]
$350-575
∘
-
13 DoF
-
-
-
FLEXI [ 12 ]
$2.5K
∘
∘
4 DoF, tablet
∙
-
∘
Reachy-M
$299-449
-
∙
6 DoF
-
∘
∘
M (ours)
$325
∙
∙
5 DoF, LCD
∙
∙
∙ ∗
TABLE I: Comparison of Social and Expressive Robot Platforms
Fig. 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.
Fig. 3: Illustration of M narrating a children’s story with synchronized multi-modal expressive behaviors produced leveraging generative AI pipelines.
Fig. 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.
Fig. 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.
Fig. 6: Illustration of M ’s capacitive touch sensing integrated beneath the exterior shell. The robot detects physical contact as an interaction signal: a brief tap (center) can indicate readiness to engage, while sustained contact (right) can be used for structured activities, enabling embodied interaction without additional visible instrumentation.
Fig. 7: Illustration of M delivering a two-way conversational positive psychology coaching session. M guides a structured gratitude practice through open-ended questions, empathetic listening with embodied acknowledgment, and context-aware follow-up dialogue maintained across multiple conversational turns.
Fig. 8: M in real-world deployment: participatory design workshops (left) and week-long autonomous home deployments (center, right).
Social robotics studies robots that interact with people through social communication, assistance, collaboration, and behavior adapted to human contexts.
Trust remains a critical barrier to the effective integration of Artificial Intelligence (AI) into human-centric domains. Disembodied agents, such as voice assistants, often fail to establish trust due to their inability to convey non-verbal social cues. This paper introduces the architecture of Botson: an anthropomorphic social robot powered by a large language model (LLM). Botson was created as a low-cost and accessible platform for social robotics research.
Samuel Bellaire, Abdalmalek Abu-raddaha, Natalie Kim +3
Programming small social robots from natural-language instructions requires more than invoking isolated APIs. Interactive tasks combine reactive physical behaviors with stateful social behaviors, while existing interfaces often require developers to manually compose APIs into skills, configure their parameters, bind sensor events to skills, and manage task states at runtime. We present MistyPilot, a multi-agent LLM framework that interprets high-level natural-language instructions and orchestrates the corresponding skills on the Misty social robot. A Task Router dispatches each instruction to one of two specialized agents: a Physically Interactive Agent for sensor-triggered robot control and direct skill invocation, and a Social Interaction Agent for dialogue-oriented task-state management and context-dependent multimodal response generation. To improve efficiency, the Social Interaction Agent reuses previously generated results when applicable and invokes full generation otherwise. We evaluate MistyPilot on five component-level suites, with sensor bindings and skill invocations executed on the physical Misty robot, and a preliminary user study with 12 participants. MistyPilot attains high accuracy on routing, sensor-skill binding, task-state parsing, result reuse, and skill extension up to 100 skills, and lower variance than an otherwise identical single-agent baseline, while participants report positive perceptions of usability and interaction quality. The code will be made publicly available via the project page.
Existing teleoperation systems are often tailored to specific robot hardware and task domains, limiting their scalability and adaptability. We present ModPack, a modular and extensible teleoperation system designed to support diverse robot embodiments and task requirements within a unified framework. At the core of ModPack is a self-contained wearable "backpack" that integrates onboard computation, power, communication, and data storage. Built on top of this shared interface, the system supports plug-and-play capability modules including joint-level teleoperation with haptic feedback, mobile manipulation, and active perception. Experiments across two distinct robot platforms and real-world mobile manipulation tasks demonstrate that ModPack provides a flexible and reusable framework for data collection and policy learning and collects higher quality data than a robot-free alternative. To support future research, we open-source the complete hardware design and software stack. Project website: https://modpack-robotics.github.io/