Voice interaction represents one of the most exciting frontiers in robotics, and with Reachy Mini's advanced AI capabilities, creating a truly conversational robot has never been more accessible. This comprehensive guide will walk you through implementing sophisticated voice control and natural language understanding in your Reachy Mini robot.
Understanding Speech Recognition Architecture
Modern voice interaction systems rely on a multi-layered architecture that processes audio input through several stages. For Reachy Mini, we can leverage both cloud-based and on-device speech recognition solutions to create responsive and intelligent voice interactions.
Key Components: Speech recognition requires audio capture, noise filtering, speech-to-text conversion, natural language understanding, response generation, and text-to-speech synthesis.
Setting Up Audio Hardware
Before implementing voice interaction, ensure your Reachy Mini has proper audio hardware. Most setups benefit from an external USB microphone for better audio quality and noise cancellation. Position the microphone to minimize mechanical noise from the robot's servos.
import speech_recognition as sr
import pyttsx3
from reachy_sdk import ReachySDK
import threading
import time
class VoiceController:
def __init__(self):
self.reachy = ReachySDK(host='localhost')
self.recognizer = sr.Recognizer()
self.microphone = sr.Microphone()
self.tts_engine = pyttsx3.init()
self.setup_voice()
def setup_voice(self):
# Configure text-to-speech voice
voices = self.tts_engine.getProperty('voices')
self.tts_engine.setProperty('voice', voices[0].id)
self.tts_engine.setProperty('rate', 150)
self.tts_engine.setProperty('volume', 0.8)
Implementing Natural Language Processing
Natural Language Processing (NLP) transforms raw speech into actionable commands for your robot. By integrating libraries like spaCy or NLTK, you can create sophisticated command parsing that understands context, intent, and entity recognition.
Intent Recognition System
An intent recognition system categorizes user commands into specific actions your robot can perform. This allows for flexible voice commands that don't require exact phrasing.
import spacy
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
class IntentRecognizer:
def __init__(self):
self.nlp = spacy.load('en_core_web_sm')
self.intents = {
'movement': ['move', 'go', 'turn', 'rotate', 'forward', 'back'],
'gesture': ['wave', 'point', 'grab', 'reach', 'gesture'],
'information': ['tell', 'what', 'how', 'explain', 'status'],
'control': ['stop', 'pause', 'resume', 'reset', 'home']
}
self.setup_classifier()
def extract_intent(self, text):
doc = self.nlp(text.lower())
tokens = [token.lemma_ for token in doc if not token.is_stop]
for intent, keywords in self.intents.items():
if any(keyword in tokens for keyword in keywords):
return intent, self.extract_parameters(doc)
return 'unknown', {}
def extract_parameters(self, doc):
parameters = {}
for ent in doc.ents:
if ent.label_ == 'CARDINAL':
parameters['number'] = int(ent.text)
elif ent.label_ == 'GPE':
parameters['location'] = ent.text
return parameters
Real-Time Voice Processing
Creating responsive voice interaction requires careful handling of audio streaming and real-time processing. Implementing proper threading ensures voice recognition doesn't block robot movement or other operations.
Performance Note: Voice recognition can be computationally intensive. Consider using a dedicated processing thread and implementing command queuing for complex operations.
Continuous Listening Implementation
Continuous listening allows your robot to respond to voice commands without manual activation. This creates a more natural interaction experience while managing system resources efficiently.
def start_continuous_listening(self):
def listen_continuously():
with self.microphone as source:
self.recognizer.adjust_for_ambient_noise(source)
while self.listening_active:
try:
with self.microphone as source:
# Listen for audio with timeout
audio = self.recognizer.listen(source, timeout=1, phrase_time_limit=5)
# Process in background thread
threading.Thread(
target=self.process_audio_command,
args=(audio,)
).start()
except sr.WaitTimeoutError:
continue
except Exception as e:
print(f"Listening error: {e}")
time.sleep(1)
self.listening_thread = threading.Thread(target=listen_continuously)
self.listening_thread.daemon = True
self.listening_thread.start()
Advanced Conversation Management
Building truly interactive robots requires conversation state management and context awareness. This allows for multi-turn dialogues and more natural interactions with your Reachy Mini.
Context-Aware Responses
Context awareness enables your robot to maintain conversation history and provide relevant responses based on previous interactions. This significantly improves the user experience and makes the robot feel more intelligent and responsive.
class ConversationManager:
def __init__(self):
self.conversation_history = []
self.current_context = {}
self.user_preferences = {}
def process_user_input(self, text, intent, parameters):
# Add to conversation history
self.conversation_history.append({
'timestamp': time.time(),
'user_input': text,
'intent': intent,
'parameters': parameters
})
# Update context based on intent
if intent == 'movement':
self.current_context['last_movement'] = parameters
elif intent == 'information':
self.current_context['info_request'] = text
# Generate contextual response
response = self.generate_response(intent, parameters)
return response
def generate_response(self, intent, parameters):
if intent == 'movement':
return f"Moving as requested. Parameters: {parameters}"
elif intent == 'gesture':
return "Performing gesture now."
elif intent == 'information':
return self.provide_information(parameters)
else:
return "I didn't understand that command. Could you try again?"
Integration with Robot Actions
The final step connects voice commands to actual robot movements and behaviors. This integration should be smooth and provide appropriate feedback to the user about the robot's actions and status.
Best Practice: Always provide audio feedback when executing voice commands to confirm the robot understood and is responding appropriately.
Command Execution Pipeline
A well-designed command execution pipeline ensures reliable performance and provides clear feedback throughout the interaction process.
def execute_voice_command(self, intent, parameters):
try:
self.speak(f"Executing {intent} command")
if intent == 'movement':
self.execute_movement(parameters)
elif intent == 'gesture':
self.execute_gesture(parameters)
elif intent == 'control':
self.execute_control_command(parameters)
self.speak("Command completed successfully")
except Exception as e:
error_message = f"Sorry, I couldn't execute that command: {str(e)}"
self.speak(error_message)
print(f"Command execution error: {e}")
def execute_movement(self, parameters):
if 'direction' in parameters:
direction = parameters['direction']
if direction in ['forward', 'back', 'left', 'right']:
# Implement movement logic
self.reachy.head.look_at(direction)
def speak(self, text):
def speak_async():
self.tts_engine.say(text)
self.tts_engine.runAndWait()
threading.Thread(target=speak_async).start()
Troubleshooting Common Issues
Voice interaction systems can face various challenges, from audio quality issues to recognition accuracy problems. Understanding common issues and their solutions helps maintain reliable performance.
Improving Recognition Accuracy
- Calibrate microphone sensitivity for your environment
- Implement noise filtering for servo motor sounds
- Use wake word detection to improve command accuracy
- Train custom models for domain-specific vocabulary
- Implement confidence scoring for command validation
Conclusion
Implementing AI-powered voice interaction transforms your Reachy Mini from a programmable robot into an intelligent companion capable of natural conversation and responsive command execution. By combining speech recognition, natural language processing, and contextual awareness, you create engaging and practical voice-controlled robotics applications.
The techniques covered in this guide provide a solid foundation for voice interaction development. As you advance, consider exploring more sophisticated NLP models, custom wake word detection, and multi-language support to further enhance your robot's conversational capabilities.
Next Steps: Experiment with different TTS voices, implement emotion detection in speech, and explore integration with large language models for more sophisticated conversations.