RobotiTalk — Building Natural Language Interfaces for Robotics

RobotiTalk Tutorials: Step-by-Step Projects for Speech-Enabled Robots

Introduction

RobotiTalk is a toolbox for adding conversational capabilities to robots—speech recognition, natural language understanding, and speech synthesis—so machines can interact with people naturally. This tutorial collection walks you through practical, incremental projects that take you from a basic voice-command robot to a context-aware, multi-modal conversational agent.

What you’ll need

• Hardware: A robot platform (e.g., Raspberry Pi-based robot, TurtleBot, or similar), USB microphone or microphone array, speakers, optional camera.
• Software: Python 3.8+, RobotiTalk SDK (or equivalent speech/NLU libraries), speech-to-text (STT) engine (local like VOSK or cloud like Google Speech-to-Text), text-to-speech (TTS) engine (e.g., Coqui TTS, eSpeak, or cloud TTS), and an optional NLU library (e.g., Rasa or Duckling for entity parsing).
• Networking: Local network and internet for cloud services if used.
• Skills: Basic Python, Linux command line, understanding of ROS if using ROS-based platforms.

Project 1 — Voice Command Relay (Beginner)

Goal: Make the robot respond to simple voice commands (move, stop, turn).

Steps:

1. Install STT and TTS libraries and test microphone input.
2. Create a Python script to capture audio and convert to text.
3. Map recognized phrases to robot control functions (e.g., “forward” → move forward).
4. Add TTS feedback: robot confirms commands with short responses.
5. Test in a safe, open area and iterate on command vocabulary.

Tips:

• Use confidence thresholds from the STT engine to avoid false triggers.
• Keep command phrases short and distinct.

Project 2 — Wake Word and Continuous Listening (Early Intermediate)

Goal: Add a wake word to prevent accidental activation and enable short multi-turn exchanges.

Steps:

1. Integrate a wake-word engine (e.g., Porcupine or Snowboy alternative).
2. Run wake-word detection continuously with low CPU overhead.
3. After wake word, open a brief listening window for commands.
4. Implement a finite-state dialog manager to handle short exchanges (confirmation, error recovery).
5. Add a timeout to return to sleep mode.

Tips:

• Use visual or audio indicators when the robot is listening.
• Optimize buffer sizes to reduce latency.

Project 3 — Intent Recognition and NLU (Intermediate)

Goal: Parse user intent and entities so the robot can handle varied phrasing.

Steps:

1. Choose an NLU framework (Rasa, spaCy with custom intent classifier, or cloud NLU).
2. Define intents (e.g., Navigate, Inform, AskStatus) and entities (location, object).
3. Collect sample utterances and train the intent model.
4. Hook NLU output into the robot’s action planner to execute tasks.
5. Add fallback handling for low-confidence predictions.

Tips:

• Start with a small set of intents and expand as you collect real user data.
• Log misclassifications to improve training data.

Project 4 — Contextual Dialogs and Memory (Advanced)

Goal: Maintain context across turns and remember user preferences or recent interactions.

Steps:

1. Implement a dialog state tracker to persist context variables (last target location, user name).
2. Add slot-filling flows for multi-step tasks (e.g., booking a service or fetching items).
3. Store short-term memory in RAM and long-term preferences in a lightweight database (SQLite).
4. Use context to disambiguate commands (e.g., “Bring it to me” — resolve what “it” refers to).
5. Test edge cases: interruptions, overlapping intents, corrections.

Tips:

• Keep memory size and access fast; prune stale entries.
• Design explicit confirmation steps for critical actions.

Project 5 — Multi-Modal Interaction and Natural Speech (Advanced)

Goal: Combine speech with vision and gestures for richer interaction.

Steps:

1. Integrate camera-based object detection (YOLO, MobileNet) to resolve references like “that cup.”
2. Use gaze/LED cues to indicate attention and combine with speech output.
3. Implement prosody and expressive TTS for more natural responses.
4. Sync simple robot gestures or movements with spoken phrases for emphasis.
5. Conduct user testing to refine timing and multimodal cues.

Tips:

• Keep multimodal responses synchronized within 300–500 ms to feel natural.
• Ensure fallback behaviors when one modality fails.

Safety and Privacy Considerations

• Always include physical safety checks before executing movement commands.
• Implement explicit user consent for recording or sending audio to cloud services.
• Allow users to disable data logging and delete stored interactions.

Debugging and Evaluation

• Log transcripts, intents, confidence scores, and system actions.
• Use unit tests for NLU components and simulated dialogs for regression testing.
• Measure latency end-to-end and aim for sub-second responses for natural interaction.

Next Steps and Extensions

• Add language switching and multilingual support.
• Integrate with calendar, smart home APIs, or messaging platforms.
• Explore on-device ML for reduced latency and improved privacy.

Conclusion

These step-by-step RobotiTalk tutorials guide you from simple voice commands to a full conversational, multimodal robot. Start small, measure performance, and iterate using real user interactions to refine intents, dialogs, and behaviors.