How SLAM Navigation and Large AI Models Are Redefining Robot Intelligence
Imagine a robot that goes beyond just following simple directions like "Go here" or "Go there." What if it could also understand the deeper intent behind your words, like "Go explore and find out what's interesting" or "Go see what's out there and decide what to do." This is where human-robot interaction evolves from basic commands to a dynamic partnership, full of unexpected possibilities.
Meet MentorPi, an intelligent robot built on Raspberry Pi 5 and ROS2, deeply integrated with multimodal large AI models. It combines precise low-level motion control, powerful mid-level environmental perception, and high-level cognitive thinking into a seamless, unified system.
To truly experience the exciting possibilities of this integration, no complex assumptions are needed—just watch how it handles a curiosity-driven task like this:
"Hey MentorPi, head to the zoo and see what animals are there, then go to the grocery and check out what fruits you can find, finally, head to the soccer field because I want to play football."
"Hey MentorPi, head to the zoo and see what animals are there, then go to the grocery and check out what fruits you can find, finally, head to the soccer field because I want to play football."
This sequence presents a multi-layered challenge: accurate point-to-point navigation, specific visual recognition upon arrival, and a complete feedback loop for the final task. MentorPi’s response is a flawless showcase of how multimodal technology collaborates perfectly.
First, it’s about task understanding and planning
When the command is received through the AI voice interaction module, the built-in language model begins to analyze it in depth. Rather than simply matching keywords, it understands the overall meaning: recognizing that "zoo," "grocery," and "soccer field" are three destinations to be visited in sequence, and that each location is linked to a specific visual task—“identifying animals,” “listing fruits,” and interpreting the intent to “play football” to confirm the task is complete. This process equips the robot with a brain that can truly understand human exploratory intentions.
💡Tips: Get all resources from MentorPi tutorials or Hiwonder GitHub
Next comes the seamless integration of autonomous navigation and scene understanding.
Once the internal task queue is generated, MentorPi’s SLAM navigation system springs into action. Using LiDAR and pre-existing maps, it plans the optimal route connecting the three destinations, navigating smoothly while avoiding obstacles and ensuring each target area is reached in turn.
In the zoo: Once MentorPi reaches the zoo area, the task shifts from navigating to observing. This is when MentorPi’s visual AI model takes charge. Its 3D depth camera scans the surroundings, and what sets it apart is its ability to go beyond simply recognizing animals—it classifies and describes them in fine detail. It then generates a comprehensive report: "There are models of a giraffe, cow, kangaroo, tiger, deer, and lion." This demonstrates its transition from seeing to understanding, offering a deeper semantic interpretation of the surroundings.
In the grocery: Similarly, after arriving at the grocery, the visual AI model is activated once again, this time focusing on scanning and recognizing the fruits category. It can precisely identify and differentiate between various types of fruits among a wide array of products. The robot then provides a detailed response: "The grocery has a variety of fruits, including apples, bananas, grapes, oranges, and more." It even adds, "Pick whichever you like best," reflecting its ability to interact based on common sense.
In the soccer field: When MentorPi finally navigates to the soccer field and reports, "Heading straight to the soccer field, let’s kick off," it’s not just the completion of a navigation task—it’s the final confirmation of the entire sequence of commands. By arriving at this location, it recognizes that the action of reaching the soccer field has fulfilled the intent to play football, creating the right conditions to complete the task and closing the loop on the entire task chain.
To sum up, by integrating text, vision, and speech into its multimodal large AI models, MentorPi brings cross-modal intelligence to life, empowering it with the ability to hear, see, move, and think. Its performance throughout this exploration task vividly showcases the depth of research and technical integration behind its engaging, interactive experience. MentorPi perfectly combines the precise spatial data from SLAM with the rich semantic insights provided by its large AI models.
💡You May Also Like: Why MentorPi is the Best Choice for ROS Beginners
For students and robotics enthusiasts, it serves as an ideal hands-on platform for exploring advanced topics like vision-language-navigation. It simplifies the implementation of complex human-robot interactions, making it easier for anyone to learn and develop cutting-edge technologies like SLAM, 3D vision, and large AI models. This is where the real magic happens, merging SLAM navigation with large AI models to take robots beyond simple tools, turning them into interactive partners that enhance our exploration of the world.
