Do you ever see an ant skittering across your kitchen counter and wonder how this little bug overcame all the obstacles in your home just to get there? Insects use their tiny nervous systems to walk with surprising dexterity - a skill that could be transferred to robots looking to do more productive things than just finding breadcrumbs in your kitchen.
An international team of researchers led by Tohoku University in Japan and the Vidyasirimedhi Institute of Science and Technology (VISTEC) in Thailand trained AI on the walking cycle of a stick insect to find the best strategy for walking on different surfaces. This was then successfully implemented in a six-legged robot that learned to walk in just an hour. In addition, the robot was able to navigate uneven terrain and adapt to a missing limb. At treacherous disaster sites where wheeled robots are unable to enter, these adaptive six-legged robots could be genuine life-savers.
© Dai Owaki, 2026
Stick insects, named for their uncanny resemblance to a small stick, are a classic model for walking research. The team used an open data source covering just three or four steps of the insect. From that data, an AI worked out two things at the same time. First, what the insect seems to be aiming for when it walks (called the "reward"). Second, how the legs should move to achieve it.
Most work asks how to make a robot walk. This study asks what walking is for.
"We never told the robot how to walk," explains Dai Owaki, Associate Professor at Tohoku University. "We asked what the insect was trying to achieve, and let the robot chase the same thing entirely on its own."
The robot learned to walk three times faster than with a standard reward. Giving the robot specific instructions about how to move each leg is a slow design process that has to be redone for every new robot. In contrast, this approach removes that step by giving the robot an animal teacher to learn from. The robot learns two categories: one part holds what is true for any body. The other part holds information that belongs to one particular machine. This split allows for the result to be moved to other robots. This means each new machine doesn't have to start from zero, which could lead to cheaper and faster production.
"It's remarkable that a few steps from a single stick insect were enough to find a principle that works on a machine five times its size," says Owaki.
The study investigated an inverse reinforcement learning method where AI isn't explicitly told how to walk - it learns from trying to understand the goal of an ideal example. In the current study, it is a stick insect, but other animals with high dexterity could also serve as models. The researchers note that adding memory so the robot can learn and build experience over time will be their next step towards developing highly mobile robots who may one day aid in disaster response efforts.
- Publication Details:
Title: From Insect Behavior to Transferable Robot Locomotion: Inferring Embodied Locomotor Principles from Limited Data via Adversarial Inverse Reinforcement Learning
Authors: Yuchen Wang, Chuthong Thirawat, Mitsuhiro Hayashibe, Poramate Manoonpong, Dai Owaki
Journal: Bioinspiration & Biomimetics (IOP Publishing)
