Robotic systems are often designed for specific tasks and operate with fixed body structures. When requirements change, engineers may have to redesign the hardware to perform new functions.
Cai’s research explores an alternative approach: creating a common set of robotic building blocks that can be assembled and reassembled to meet different needs. Taking inspiration from the diverse forms found in nature, the soft robotic system is built from identical modules and rearranged into different structures and movement patterns to show how robots might adapt to changing tasks and environments. Cai’s work was recently published in Science Advances.
The research contributes to the growing field of physical intelligence, which examines how a robot’s structure and materials influence its behavior and adaptability.
“I believe soft, modular, and bio-inspired robotics offer a promising pathway toward physical intelligence, where intelligence emerges not only from computation but also from the robot’s physical design, compliance, and ability to adapt to its environment,” Cai said.
Looking ahead, the team will study how robotic systems can decide for themselves when and how to reconfigure. By combining adaptable hardware with learning and control algorithms, the researchers aim to better understand how physical adaptability and computational intelligence work together.
For Cai, these awards encourage him to continue pursuing big questions in robotics.
“These awards are not only a recognition of what we have achieved,” Cai said, “but also an encouraging sign of what lies ahead.”
