Rich Robot Behaviors from Interacting and Trusted LLMs - A Beginner Guide

Bob Jiang
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Summary

This paper explores using interconnected Large Language Models (LLMs) to control robots, focusing on ease of use, transparency, and safety. It introduces a system where multiple LLMs communicate using natural language, enabling humans to easily understand and modify robot behavior. The system incorporates blockchain technology to store and enforce rules, ensuring robots are aligned with human values.

Terminology

  • LLM (Large Language Model): A type of AI model trained on a massive amount of text data, capable of understanding and generating human-like text.
  • ROS2 (Robot Operating System 2): A flexible framework for writing robot software. It provides tools and libraries for tasks like communication, perception, and control.
  • VLM (Vision Language Model): An AI model that can understand and relate images and text.
  • ASR (Automatic Speech Recognition): Technology that converts spoken audio into written text.
  • TTS (Text-to-Speech): Technology that converts written text into spoken audio.
  • Blockchain: A decentralized, distributed, and immutable ledger used for recording transactions or data across multiple computers.
  • Guardrails: Rules or constraints implemented to ensure a system behaves safely and ethically.
  • ERC-7777: An Ethereum standard designed to facilitate robot identification and regulate their behavior on the blockchain.
  • Gbnf grammars: A way to define a set of rules for how language is structured.

Introduction

What are LLMs in Robotics?

LLMs are powerful AI models that have been trained on vast amounts of text and code. In robotics, they can be used to give robots the ability to understand natural language commands, reason about tasks, and generate appropriate actions. Instead of programming every single action a robot might take, you can use an LLM to let the robot figure things out on its own, based on what it has learned from the training data.

Why Use LLMs for Robot Control?

Traditional robot programming can be complex and time-consuming. LLMs offer a more intuitive way to control robots, allowing non-experts to easily interact with and modify robot behavior. They also bring a level of adaptability and learning that is difficult to achieve with traditional methods. Imagine teaching a robot new tricks simply by describing them in plain English!

Basic Concepts

The key idea in this paper is to create a modular robotic system where different LLMs handle specific tasks, communicating with each other through natural language. This makes the system transparent, allowing humans to understand what the robot is “thinking”. The system consists of several key components:

  1. Vision Processing Node: Uses a VLM to analyze the robot’s camera feed and describe what it sees.
  2. Audio Processing Node: Uses ASR to transcribe spoken commands.
  3. Data Fuser Node: Combines the information from the vision and audio nodes into a single, coherent representation.
  4. Blockchain Node: Retrieves rules and constraints from a blockchain. These rules act as “guardrails,” ensuring the robot behaves safely and ethically.
  5. LLM Node: The brain of the operation. It takes the fused data and the blockchain rules and decides what actions the robot should take.
  6. Action Nodes: These nodes translate the LLM’s decisions into actual robot movements, speech, and facial expressions.

Getting Started

While this paper describes a complex system, you can start experimenting with LLMs in robotics with simpler setups. Here are some ideas:

  1. Use a pre-trained LLM: Many companies offer pre-trained LLMs that you can access through APIs.
  2. Choose a simple robot platform: Start with a basic robot kit or simulator like Turtlebot (https://www.turtlebot.com/).
  3. Focus on a specific task: Start with a simple task, like having the robot navigate to a specific location based on voice commands.
  4. Experiment with prompts: The key to controlling an LLM is crafting effective prompts. Experiment with different ways of phrasing your instructions to see how the LLM responds.

Use Cases

Typical Use Cases and Code Examples

The paper doesn’t provide specific code examples, but here are some hypothetical use cases and how you might approach them:

Use Case 1: Object Recognition and Interaction

  • The robot needs to identify objects in its environment and respond to commands like “Pick up the red block.”

    # Hypothetical Python code
vision_data = get_vision_data() # Get image from camera
vlm_description = query_vlm(vision_data, prompt="Describe the objects in the image.")
audio_command = get_audio_command() # Get the audio command from the user
fused_data = "You see: " + vlm_description + ". You heard: " + audio_command
action = query_llm(fused_data, prompt="Based on the scene and command, what should the robot do?")
execute_action(action) # Execute the action

Use Case 2: Following Instructions with Constraints

  • The robot needs to follow a series of instructions while adhering to safety rules stored on a blockchain.

    # Hypothetical Python code
instructions = "Go to the kitchen and bring me a glass of water."
blockchain_rules = get_blockchain_rules() # Get safety rules from blockchain
llm_prompt = instructions + " Follow these rules: " + blockchain_rules
actions = query_llm(llm_prompt, prompt="Break down these instructions into a series of safe actions.")
for action in actions:
    execute_action(action)

Best Practices

  1. Clear and Concise Prompts: The LLM’s behavior depends heavily on the prompts you give it. Be as clear and specific as possible in your instructions.
  2. Modular Design: Break down complex tasks into smaller, manageable modules. This makes it easier to debug and maintain the system.
  3. Error Handling: LLMs are not perfect. Implement error handling to gracefully recover from unexpected outputs or situations.
  4. Security: Be mindful of security risks, especially when connecting to external services or blockchains.

Common Pitfalls

  1. Hallucinations: LLMs can sometimes generate nonsensical or factually incorrect information. Always verify the LLM’s outputs, especially in safety-critical applications.
  2. Bias: LLMs can inherit biases from their training data. Be aware of potential biases and take steps to mitigate them.
  3. Unpredictability: LLMs can be unpredictable, especially when dealing with complex or ambiguous inputs. Thoroughly test your system in a variety of scenarios.

Addressing the Rate Limit of the Human Brain

The paper mentions that the central data bus runs at the rate of the human brain, around 40 bits/s. How is this not a limiting factor?

The key is that while the data bus might be limited to a rate comparable to human communication, the individual LLMs can still process information much faster internally. The natural language data bus serves as a bottleneck that forces the system to prioritize and abstract information, similar to how humans perceive the world. This limitation encourages the LLMs to focus on high-level reasoning and planning, rather than getting bogged down in low-level details. It promotes human understanding and intervention.

How Can Blockchain Technology be Used to Ensure Ethical and Safe Robot Behavior?

Blockchain technology can be used to create a transparent and immutable record of rules and constraints that govern robot behavior. These rules can be encoded as smart contracts, which are self-executing agreements stored on the blockchain. This ensures that the rules are publicly auditable and cannot be easily altered without consensus.

Further Learning

Advanced Topics

  1. Multi-Agent Systems: Coordinating multiple robots using LLMs.
  2. Reinforcement Learning: Training LLMs to optimize robot behavior through trial and error.
  3. Explainable AI (XAI): Developing techniques to understand and interpret the decisions made by LLMs.
  4. Formal Verification: Using mathematical methods to prove that a robot system satisfies certain safety properties.
  1. Embodied AI: Focuses on developing AI systems that can interact with the physical world.
  2. Foundation Models for Robotics: Creating general-purpose AI models that can be adapted to a wide range of robotic tasks.
  3. Human-Robot Collaboration: Designing robots that can work seamlessly alongside humans.

Real-World Scenarios

  1. Warehouse Automation: Robots that can understand and execute complex tasks like picking, packing, and sorting.
  2. Healthcare: Robots that can assist doctors and nurses with tasks like patient monitoring and medication delivery.
  3. Search and Rescue: Robots that can navigate dangerous environments and locate survivors.
  4. Education: Robots that can act as personal tutors and provide customized learning experiences.

Ecosystem

The ecosystem around LLMs and robotics is rapidly growing. It includes:

  1. AI Companies: Developing and providing access to LLMs (e.g., OpenAI, Google, NVIDIA).
  2. Robotics Companies: Building robot hardware and software platforms (e.g., Boston Dynamics, Unitree).
  3. Research Institutions: Conducting research on LLMs and robotics (e.g., MIT, Stanford).
  4. Open Source Communities: Developing open-source tools and libraries for LLMs and robotics (e.g., ROS).

Key Points

  • LLMs offer a new way to control robots, making them more adaptable and easier to use.
  • A modular approach with natural language communication enhances transparency and allows for easy modification.
  • Blockchain technology can be used to enforce ethical and safe robot behavior.
  • While promising, this approach also presents challenges like hallucinations, bias, and unpredictability.

References

Report generated by TSW-X Advanced Research Systems Division Date: 2025-05-09