Robotics (AI)
Robotics in AI refers to the integration of machine learning models into physical robots to enable perception, decision-making, and control. Operators encounter this when deploying models like YOLO for object detection or LLMs for natural language instructions on edge devices such as NVIDIA Jetson or Raspberry Pi. Key constraints include latency (real-time control requires <100ms inference), power consumption, and model size (quantized models like YOLOv8n at FP16 fit in 4GB VRAM).
Practical example
A robot arm using a local LLM (e.g., Llama 3.2 3B Q4) on an NVIDIA Jetson Orin (8GB VRAM) for natural language pick-and-place commands. The model processes speech-to-text, then outputs joint angles via a fine-tuned control head. Inference latency of ~50ms allows smooth operation.
Workflow example
In LM Studio, an operator loads a quantized YOLOv8n model (6MB, FP16) for real-time object detection on a USB camera feed. The model runs at 30 FPS on an RTX 3060. For LLM-based control, they use Ollama with a custom Modelfile to set context length to 2048 and temperature to 0.2 for deterministic commands.
Reviewed by Fredoline Eruo. See our editorial policy.