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.
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.
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!
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:
While this paper describes a complex system, you can start experimenting with LLMs in robotics with simpler setups. Here are some ideas:
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)
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.
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.
The ecosystem around LLMs and robotics is rapidly growing. It includes:
Report generated by TSW-X Advanced Research Systems Division date: 2025-05-09
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.
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.
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!
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:
While this paper describes a complex system, you can start experimenting with LLMs in robotics with simpler setups. Here are some ideas:
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)
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.
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.
The ecosystem around LLMs and robotics is rapidly growing. It includes:
Report generated by TSW-X Advanced Research Systems Division date: 2025-05-09