Botson: An Accessible and Low-Cost Platform for Social Robotics Research

Samuel Bellaire; Abdalmalek Abu-raddaha; Natalie Kim; Nathan Morhan; William Elliott; Samir Rawashdeh

cs.ROFeb 23, 2026

Botson: An Accessible and Low-Cost Platform for Social Robotics Research

Authors: Samuel Bellaire, Abdalmalek Abu-raddaha, Natalie Kim, Nathan Morhan, William Elliott, Samir Rawashdeh

Organizations: University of Michigan-Dearborn

Abstract

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.

Figures & tables

Fig. 1: Botson in Idle State (left) and Performing a Waving Gesture (right)

Fig. 2: High-Level Block Diagram of Botson

Fig. 4: Evaluation of Helpfulness of AI Agents by Users

Fig. 5: Evaluation of Trust in Voice-Only vs. Embodied AI

Fig. 6: Usefulness of Gestures in Engagement

Fig. 7: Comfort Level of Users with Embodied Robot

Social Robotics

Social robotics studies robots that interact with people through social communication, assistance, collaboration, and behavior adapted to human contexts.

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MistyPilot: An Agentic Fast-Slow Thinking LLM Framework for Misty Social Robots

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State University of New York at Buffalo, Buffalo, NY 14260, USA

Agentic AI for Robotics LLM Agent Orchestration Large Language Model-Based Robot Planning Robotic Tool Use Social Robotics Human-Robot Interaction

Open paper AskPDF arXiv

With the availability of open APIs in social robots, it has become easier to customize general-purpose tools to meet users' needs. However, interpreting high-level user instructions, selecting and configuring appropriate tools, and executing them reliably remain challenging for users without programming experience. To address these challenges, we introduce MistyPilot, an agentic LLM-driven framework for autonomous tool selection, orchestration, and parameter configuration. MistyPilot comprises two core components: a Physically Interactive Agent (PIA) and a Socially Intelligent Agent (SIA). The PIA enables robust sensor-triggered and tool-driven task execution, while the SIA generates socially intelligent and emotionally aligned dialogue. MistyPilot further integrates a fast-slow thinking paradigm to capture user preferences, reduce latency, and improve task efficiency. To comprehensively evaluate MistyPilot, we contribute five benchmark datasets. Extensive experiments demonstrate the effectiveness of our framework in routing correctness, task completeness, fast-slow thinking retrieval efficiency, tool scalability,and emotion alignment. All code, datasets, and experimental videos will be made publicly available on the project webpage.

Abstract

Social Robotics

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