The ChatGPT Moment for Robotics: A Quick Overview

Large Language Models (LLMs) like GPT are transforming robotics from rigid, rule-based systems into adaptable, context-aware machines. This short intro outlines how we moved from GOFAI-era logic to today’s language-powered embodied intelligence.

Before GPT: The GOFAI Era

• Robots depended on hand-crafted rules and deterministic logic.
• Poor at handling ambiguity or unfamiliar scenarios.
• Strong at narrow tasks but lacked true understanding.

After GPT: The Paradigm Shift

• Foundation models enable reasoning, generalization, and fluid dialogue.
• Robots can decompose tasks, clarify unclear instructions, and adapt mid-execution.
• Perception evolves into cognition — machines understand why, not just what.

LLMs in Robotics Today

1. Task planning from high-level commands.
2. Ambiguity handling through interactive clarification.
3. On-the-fly code generation for robotic behaviors.
4. Scene understanding using contextual cues.
5. Natural language explanations for decisions and errors.

What’s Next

• Multi-modal sensory fusion.
• Continual learning beyond initial training.
• On-device LLM deployment for faster, private inference.
• Stronger ethical and safety frameworks for autonomous systems.

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