Home · Module 3 · NLP for Robots
From speech to robot action
Try typing a command — see how a simple intent classifier turns your words into something the robot can do. This is a tiny version of what runs inside Pepper.
Talk to the robot
NLP pipeline (last message)
Send a message to see how it's processed.
Recognized intents
How a robot understands you
This demo uses the simplest possible NLP: keyword matching with confidence scoring. Real systems like Pepper's NAOqi ALSpeechRecognition or Rasa use a pipeline that's conceptually similar but much richer:
1. Speech → Text
A neural acoustic model converts the microphone waveform into a transcript. Pepper uses Nuance / Vocon.
2. Tokenize & Normalize
Split into words, lowercase, remove punctuation, optionally stem ("waving" → "wave").
3. Intent Classification
A classifier (keywords, TF-IDF + SVM, or BERT) picks the most likely intent from a defined set.
4. Entity Extraction
Pull out parameters: "go forward 2 meters" → intent=move, direction=forward, distance=2m.
5. Action Mapping
Intent + entities → a specific NAOqi call. Move forward → ALMotion.moveTo(2, 0, 0).
6. Response Generation
Pick a reply ("On it!"), pass to TTS, speak. Optionally launch the behavior.
From keywords to LLMs — Modern Pepper deployments often combine classical intent recognition (fast, cheap, predictable) with an LLM fallback for open-ended questions. The trick is knowing when to escalate: a "wave at me" command shouldn't go to a 70B-parameter model.