4-2-voice-to-action-pipeline

The Voice-to-Action Pipeline

The core of a VLA system that responds to spoken commands is the Voice-to-Action pipeline. This pipeline transforms ephemeral human speech into concrete, executable robot commands. It typically involves three main stages: voice recognition, intent extraction, and task breakdown.

Whisper for Voice Recognition

The first step in any voice-controlled system is converting spoken words into text. OpenAI Whisper is an excellent general-purpose speech recognition model that excels at this task. It is robust to accents, background noise, and technical language, making it highly suitable for robotics applications.

Whisper can be run locally on your machine (including on a GPU for faster inference) or accessed via an API. For robotics, a local, real-time implementation is often preferred.

How it works (simplified):

1. Audio Input: The robot's microphone captures raw audio.
2. Preprocessing: The audio is processed (e.g., sampled, filtered) and fed into the Whisper model.
3. Transcription: Whisper outputs a text string representing the spoken command.

Code Example (Python with whisper library):

import whisper

def transcribe_audio(audio_file_path):
    """
    Transcribes an audio file using OpenAI's Whisper model.
    """
    model = whisper.load_model("base") # or "small", "medium", "large" for better accuracy
    result = model.transcribe(audio_file_path)
    return result["text"]

# Example usage:
# if __name__ == "__main__":
#     text = transcribe_audio("my_voice_command.wav")
#     print(f"Transcription: {text}")

This transcribed text then becomes the input for the next stages of the pipeline.

Intent Extraction from Language

Once we have the command in text format, the robot needs to understand what the user intends for it to do. This is called intent extraction. It involves parsing the natural language command to identify the core action, objects involved, and any modifiers or constraints.

For example, if the command is "Go to the kitchen table and pick up the blue mug":

• Intent: Navigate, Pick Up
• Target Locations: Kitchen table
• Target Objects: Blue mug
• Constraints: Color (blue)

LLMs are exceptionally good at intent extraction. You can prompt an LLM with the transcribed command and ask it to output a structured representation of the intent.

LLM Prompt Example:

User command: "Go to the kitchen table and pick up the blue mug."

Extract the robot's intent, specifying actions, target locations, and target objects. Respond in JSON format.

{
  "actions": [
    {"type": "navigate", "location": "kitchen table"},
    {"type": "pick_up", "object": {"name": "mug", "color": "blue"}}
  ]
}

The LLM would then return a JSON object (or similar structured output) that a robotic system can easily parse.

Creating Task Breakdowns Using LLMs

Many human commands are high-level and abstract. A robot cannot directly execute "Clean the room." It needs a task breakdown—a sequence of smaller, specific, and executable actions. LLMs are powerful tools for this hierarchical planning.

Given a high-level intent, an LLM can generate a series of sub-tasks. This often involves:

• Decomposition: Breaking a large task into smaller, manageable steps.
• Ordering: Arranging these steps in a logical sequence.
• Refinement: Adding details or conditions to each step.

Example: "Clean the room" → Action Graph

Let's consider the command: "Clean the room." An LLM, perhaps with some pre-defined context about the robot's capabilities and the room's layout, could generate the following task breakdown:

{
  "high_level_command": "Clean the room",
  "task_graph": [
    {
      "id": "1",
      "action": "navigate_to_location",
      "parameters": {"location": "living_room_center"},
      "dependencies": []
    },
    {
      "id": "2",
      "action": "identify_and_localize_objects",
      "parameters": {"objects_of_interest": ["cup", "book", "remote"]},
      "dependencies": ["1"]
    },
    {
      "id": "3",
      "action": "pick_up_object",
      "parameters": {"object_id": "cup_01"},
      "dependencies": ["2"]
    },
    {
      "id": "4",
      "action": "navigate_to_location",
      "parameters": {"location": "kitchen_sink"},
      "dependencies": ["3"]
    },
    {
      "id": "5",
      "action": "place_object",
      "parameters": {"location": "kitchen_sink"},
      "dependencies": ["4"]
    },
    {
      "id": "6",
      "action": "pick_up_object",
      "parameters": {"object_id": "book_01"},
      "dependencies": ["5"]
    },
    {
      "id": "7",
      "action": "navigate_to_location",
      "parameters": {"location": "bookshelf"},
      "dependencies": ["6"]
    },
    {
      "id": "8",
      "action": "place_object",
      "parameters": {"location": "bookshelf"},
      "dependencies": ["7"]
    }
    // ... more steps for other objects
  ]
}

This structured task breakdown, effectively an action graph, can then be executed by a robot's control system. Each "action" in the graph corresponds to a low-level capability the robot possesses, often implemented as a ROS 2 Action Server, which we will explore further.