Robots Mimic Human Memory to Boost Efficiency in Smart Factories

Robots cut 30% travel time using human-like memory in smart factories

The robots could distinguish between important, real-time obstacles and unnecessary, outdated information using this human-like forgetting method. "We have mimicked the social principle of forgetting unnecessary information while retaining only important information to enable efficient movement. This study is significant in that it shows how Physical AI is evolving to resemble human behavior," said Professor Kyung-Joon Park, the study author, in the press release on September 29. Autonomous Mobile Robots (AMRs) are vital for automation in various fields, including logistics and manufacturing. However, unexpected obstacles often hinder the robot's movement in these real-world operations. Robots are typically trained to adjust paths when they encounter obstacles, such as forklifts or misplaced cargo. However, the machine's inability to forget means it continues taking unnecessary detours even after the blockage is gone. Hence, it diminishes productivity in high-stakes environments like logistics centers and smart factories.

Robot navigation improves 30% by mimicking how humans spread and forget information

A research team has developed a new "Physical AI" technology that improves the efficiency of multi-robot autonomous navigation by modeling the spread and forgetting of social issues. This achievement is expected to become a key technology for boosting the productivity of autonomous mobile robots in logistics centers, large-scale warehouses, and smart factories. The work is published in the Journal of Industrial Information Integration. The study was led by Professor Kyung-Joon Park of the Department of Electrical Engineering and Computer Science and the Physical AI Center at DGIST. Autonomous mobile robots (AMRs) play a central role in automation across logistics and manufacturing sites. However, in real-world operations, unexpected obstacles, such as forklifts, work lifts, or unexpectedly placed cargo, often disrupt smooth movement. Until now, robots have reacted only to immediate situations and adjusted their routes accordingly, leading to unnecessary detours and delays, and ultimately, diminished productivity. To address this challenge, Professor Kyung-Joon Park's team applied a unique phenomenon of human society to robots. They focused on how certain events or issues spread rapidly and are then gradually forgotten. The team mathematically modeled this process and incorporated it into a collective intelligence algorithm for robots. Consequently, the robots were able to naturally forget unnecessary information, immediately share only the important details, and achieve more efficient cooperative navigation. In the actual experiments, the team utilized the "Gazebo simulator," which replicates a logistics center environment. The results showed that the new technology increased task throughput by up to 18.0% and reduced average driving time by up to 30.1% compared to conventional ROS 2 navigation. This demonstrates that robots are no longer merely machines that avoid obstacles; they are evolving into Physical AI systems that can comprehend social principles and operate autonomously. Another valuable feature of this technology is its ease of application. It can be implemented using only 2D LiDAR without additional sensors, and has been developed as a plugin compatible with the ROS 2 navigation stack. This implies that it can be applied directly to existing autonomous navigation systems without the need for complex equipment, enabling rapid deployment in industrial settings such as drone swarms, autonomous vehicles, and logistics robots. Particularly, it is expected to play a significant role in implementing cooperative autonomous navigation systems for smart city traffic management as well as large-scale exploration and rescue operations. Professor Kyung-Joon Park stated, "We have mimicked the social principle of forgetting unnecessary information while retaining only important information to enable efficient movement. This study is significant in that it shows how Physical AI is evolving to resemble human behavior."