LLMs In Robotics

LLMs In Robotics

LLMs in robotics are language-model capabilities used around robots for planning, coding, instruction following, human interaction, tool use, and higher-level reasoning.

Key points

  • Back to Engineering's PiDog and microcontroller-code videos show a practical early pattern: use LLMs to write or assist robot-control code, then test the result against real hardware [src-076].
  • Running LLMs locally on robot-adjacent hardware is constrained by memory, compute, latency, and power; cloud LLMs add privacy and reliability questions when robots perceive homes or people [src-076].
  • LLMs can help with instructions and behaviour, but robot usefulness still depends on reliable sensors, actuators, control code, and task-specific data [src-076].
  • Gemini Robotics-ER illustrates a more specialised model-side pattern: high-level embodied reasoning can call tools, VLA models, or user functions while reasoning about physical scenes [src-039].
  • The important distinction is between a chat model that talks about a robot and a robot stack where model output is grounded in perception, constraints, and executable actions [src-076].
  • Fan argues that language-heavy VLA models are not enough for the robotics endgame because they are better at nouns and knowledge than at physics and verbs [src-082].
  • His proposed replacement does not discard language, but demotes it from the center: world/action models make vision and action first-class, with language prompting used to steer open-ended tasks [src-082].

Related entities

Related concepts

Source references

  • [src-039] Laura Graesser and Peng Xu – "Gemini Robotics-ER 1.6: Powering real-world robotics tasks through enhanced embodied reasoning" (2026-04-14)
  • [src-076] Back to Engineering (iulia) – physical AI, robotics, and data science cluster (41 videos, 2018-12-16 to 2026-05-10)
  • [src-082] Sequoia Capital — "Robotics' End Game: Nvidia's Jim Fan" (2026-04-30)

Robin Cartier perspective

This page is part of Robin Cartier's working AI knowledge graph: a practical research layer for production AI, recommendation systems, experimentation, GEO, and agentic web readiness.

The useful next step is to connect this concept back to applied product leadership and operating models.

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