What Does a "Super Brain" Bring to ROS Robots?
ROS robots equipped with high-performance hardware like LiDAR and depth cameras can already perform SLAM mapping and navigation, dynamic obstacle avoidance, and even complex tasks like 3D visual object manipulation and gesture recognition. Now, Hiwonder Technology has taken this a step further by introducing multi-modal AI large models into ROS robots like LanderPi, endowing them with a true "super brain" that pushes robotic intelligence to unprecedented heights.
I. What Is LanderPi's "Super Brain"?
Traditional ROS robots typically execute tasks based on pre-programmed routines. In stark contrast, LanderPi's "super brain" is a multi-modal AI large model deployed directly on the robot. This powerful system can simultaneously process and understand various types of information—text, vision, and speech. It supports integration with leading large models like DeepSeek, Qwen (Tongyi Qianwen), giving the robot genuine capacities for understanding, reasoning, and decision-making.
Through the synergistic work of the WonderEcho Pro AI Voice Interaction Box and the 3D depth camera, LanderPi doesn't just "hear" commands; it "understands" the intent behind them, "sees" its surrounding environment, and "thinks" about how to accomplish the task. This holistic perception and comprehension elevate the robot from a simple execution tool to an intelligent collaborative partner.
II. How Does AI Integration Truly Transform the Robot?
While traditional ROS robots often operate with perception, planning, and control as separate modules, the introduction of a multi-modal AI "super brain" achieves a qualitative leap in information fusion and intelligent decision-making.
● In Environmental Perception: From "Object Recognition" to "Scene Understanding."
The AI model allows the robot not only to identify objects but to grasp the semantics of a scene. For instance, in a soccer field environment, the robot no longer merely "detects a spherical object." It understands that "this is a soccer ball in front of the goal." This ability for scene comprehension forms a crucial foundation for performing tasks in the real world.
● In Task Planning: From "Sequential Execution" to "Autonomous Task Breakdown."
Faced with an instruction like "Please organize the red cubes into the top-left area"—which involves spatial relationships and ambiguous semantics—traditional robots often rely on manually pre-programmed action sequences. LanderPi can autonomously parse such instructions, breaking them down into a series of sub-tasks like target identification, grasping sequence planning, obstacle-avoidance navigation, and precise placement, all while dynamically adjusting the execution flow.
● In Human-Robot Interaction: From "Fixed Commands" to "Natural Dialogue."
By integrating a large language model, LanderPi breaks through the limitations of fixed voice commands. When a user says, "Please hand me the milk on the table," the robot can synchronously parse visual and linguistic information to achieve precise target localization and grasping, significantly enhancing the naturalness and efficiency of human-robot collaboration.
III. What Can You Learn by Combining Multi-Modal AI with ROS Robots?
Hiwonder's integration of multi-modal AI models into the ROS robotics platform significantly lowers the technical barrier, creating a clear path from theory to practice. Through this integrated system, learners can master the practical deployment of multi-modal fusion technology, gain a deep understanding of how large language models integrate with ROS 2, and build intelligent robots capable of environmental understanding and autonomous decision-making. This platform fosters growth in three key dimensions:
● A Clear Path from Foundation to Mastery: Through modular design and systematic curricula, Hiwonder has built a complete learning LanderPi tutorials from beginner to advanced levels. The course structure covers fundamentals like ROS 2 communication mechanisms and SLAM mapping and navigation, extending to advanced topics such as calling and fusing multi-modal large models. This creates a progressive learning loop, laying a solid foundation for exploring the field of Embodied AI.
● Fusing Theory with Practical Verification: Through hands-on projects like "Voice-Controlled Autonomous Navigation" and "3D Recognition, Grasping, and Manipulation," learners engage with real-world scenarios to understand the technical chain from AI semantic decision-making to ROS task planning and low-level execution control. This transforms abstract theoretical knowledge into tangible, debuggable systems engineering experiences. This "learning by doing" model effectively deepens understanding of intelligent robotic system workflows.
● Creative Space from Concept to Realization: The high-performance hardware ecosystem—comprising a 3D vision-enabled robotic arm, high-torque encoded motors, a TOF LiDAR, and the WonderEcho Pro voice interaction module—combined with the semantic understanding capabilities bestowed by multi-modal AI, opens up infinite possibilities for innovative projects. Learners can freely develop various Embodied AI applications on this architecture, turning creative ideas into practical solutions and achieving a true leap from technical learning to innovative application.
The multi-modal AI "super brain" is more than just a technical upgrade; it represents a fundamental transformation in a robot's cognitive capabilities. When robots can understand scene semantics, interpret human intent, and autonomously plan tasks, we are stepping into a new era of human-robot integration and intelligent collaboration. Hiwonder Technology's comprehensive platform for teaching and research allows learners to easily bridge the gap between theory and practice, cultivating the technical insight and system architecture skills needed for the future.