A Hands-on Workshop on Designing LLM-Powered Context-Aware Behavior for Companion Robots
Designing LLM-Powered Context-Aware Robot Behavior
Explore how Large Language Models (LLMs) can generate dynamic, context-aware behaviors for companion robots, enhancing human-robot interaction through expressive and embodied responses.
Why LLMs are Essential for Next-Gen Robotics
Traditional robot behavior relies on static rules, limiting adaptability. LLMs provide dynamic, nuanced responses, enabling robots to interpret complex human cues and respond intelligently and expressively.
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Increase in Robot Adaptability
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Improvement in Interaction Nuance
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Research Publications
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
AwaR(e)obot System Design
The AwaR(e)obot system integrates Human Interface, LLM Module, and Robot Execution Layer to enable expressive, dog-like physical behaviors for quadruped robots like Boston Dynamics' Spot.
Enterprise Process Flow
Human Voice Input
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Speech-to-Text Transcription
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LLM Prompt Generation
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Behavioral Marker Output
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Robot Movement Execution
LLMs as Behavior Generators
LLMs interpret conversational inputs and generate sequences of behavioral markers based on robot persona, affordance profile, and a response mapping framework. This allows for dynamic, context-aware responses, moving beyond pre-programmed scripts.
Dynamic
Context-Aware Behaviors
Designing Expressive Robot Interactions
This workshop focuses on embodied interaction design, robot expressivity, and prompt engineering. Participants will learn to craft effective LLM prompts for nuanced robot behavior generation, considering physical capabilities and emotional resonance.
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| Interaction Nuance |
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| Embodied Expression |
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System Generalizability and Future Directions
The modular design allows adaptation across robotic platforms. Future work includes developing an open-source toolkit with LLM prompt libraries and publishing research on participatory design of robot behavior.
Open-Source Toolkit & Research
An ongoing research project aims to develop an open-source toolkit based on the AwaR(e)obot framework. This will include LLM prompt libraries, behavior templates, and customization tools. Collected data from the workshop will inform a peer-reviewed paper on the participatory design of robot behavior using LLMs.
Estimate Your AI Impact
Quantify the potential time and cost savings by implementing LLM-powered solutions in your enterprise.
Annual Savings
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Hours Reclaimed
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Workshop Structure and Outcomes
The workshop provides hands-on experience in designing expressive and context-aware robotic behaviors using LLMs. Participants will leave with a practical understanding and a portfolio of user-designed scenarios.
Introduction & Demo
Overview of LLM-powered robotics, embodied interaction, and a live demonstration of the AwaR(e)obot system with Boston Dynamics' Spot.
Group Activity: Design Robot Expressions
Participants design LLM prompts and behavior sequences for daily life scenarios, defining robot persona and affordances.
Presentations & Peer Evaluations
Groups present their designs, test on the robot, and receive peer feedback on expressiveness and contextual relevance.
Closing Discussion & Reflection
A group reflection on insights, challenges, and ideas for future development and research directions.
Ready to Transform Your Robot Interactions?
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