Home Bots & Brains AI-Enabled System Improves Human-to-Robot Communication

AI-Enabled System Improves Human-to-Robot Communication

by Marco van der Hoeven

Researchers at Brown University have developed a software system that enables robots to understand and execute instructions given in plain English. This development marks a notable advance in the field of human-robot interaction.

The software was demonstrated using a robot designed to resemble a large dog. It successfully followed instructions involving a sequence of actions, such as visiting specific locations in a particular order, without requiring code-based commands. This ability distinguishes it from traditional robot navigation systems, which often rely on extensive training data to interpret human language.

Linguistic Challenges

Traditional navigation robots typically require large amounts of training data to understand and act on commands. However, this new system uses advanced large language models driven by artificial intelligence, reducing the need for such extensive training. This makes it possible for the system to execute more complex tasks and suggests potential for broader applications in various environments.

The research, detailed in a paper to be presented at the Conference on Robot Learning, focuses on the challenge of translating human language, which can be intricate and abstract, into actionable commands for robots. The system aims to facilitate more direct and efficient communication between humans and robots.

Application Potential

According to Stefanie Tellex, a computer science professor at Brown and the senior author of the study, the system could be applied to various types of mobile robots, such as drones and self-driving cars. It is designed to process detailed instructions, which could be beneficial in navigating urban environments.

The system employs AI language models to interpret instructions. It is capable of making logical deductions based on the context and constraints provided in the instructions, an essential feature for navigating complex scenarios.

Adaptability and Efficiency

One of the system’s advantages is its ability to adapt to new environments without requiring a lengthy training process. Traditional models needed specific training for each new location, whereas this system can operate with just a detailed map of the area.


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