This research introduces RDMM (Robotics Decision-Making Models), fine-tuned LLMs that enable robots to make context-aware decisions while understanding their own capabilities and limitations.

• Optimized for on-device deployment in household robotics applications
• Provides robots with enhanced contextual awareness of their environment
• Enables domain-specific decision-making capabilities for real-world tasks
• Improves robot assistance capabilities through specialized knowledge frameworks

This engineering advancement matters because it bridges the gap between powerful AI language models and practical robotics applications, making robots more capable of understanding and responding to real-world situations with appropriate actions.

RDMM: Fine-Tuned LLM Models for On-Device Robotic Decision Making with Enhanced Contextual Awareness in Specific Domains