A team of engineers has developed a four-legged robot that can decide for itself how to move through different environments, much like an animal.
Instead of relying on a separate program for every movement, the robot can automatically choose whether to walk, run, jump or leap over obstacles based on what it sees around it.
The breakthrough, developed by researchers at the Korea Advanced Institute of Science and Technology (KAIST), could help future robots work more effectively in disaster zones, industrial sites and other challenging environments.
The study was published in Science Robotics.
Four-legged robots have become increasingly popular because they can travel across rough ground much more easily than robots with wheels. However, the real world is rarely smooth or predictable.
A robot may need to climb stairs, step over rocks, cross gaps, avoid fallen branches or move along uneven forest paths. Until now, most robots could perform some of these tasks individually, but they often struggled to switch smoothly between different types of movement as conditions changed.
To solve this problem, the KAIST research team developed a new artificial intelligence control system called APT-RL, short for Action Pretrained Transformer-based Reinforcement Learning. Instead of teaching the robot only one way to move, the researchers trained it to master several different movement styles, including walking, running and jumping.
The AI then learned how to decide which movement was best for each situation and how to switch naturally between them.
Unlike many earlier projects that relied on recording the movements of people or animals, the researchers trained their robot entirely through computer simulations. In just eight minutes, the system generated the equivalent of more than 15 hours of movement data, covering many different walking and running styles.
This allowed the robot to learn efficient ways to move without the time and expense of collecting real-world motion capture data.
The team also used reinforcement learning, a form of artificial intelligence in which a computer learns by repeatedly trying different actions and improving through experience. Over time, the robot became better at choosing the safest and fastest way to move through complex environments.
To understand its surroundings, the robot was equipped with a depth camera and a LiDAR sensor. The depth camera measures the distance to nearby objects, while LiDAR uses laser pulses to create a detailed three-dimensional map of the environment. Together, these sensors allow the robot to detect obstacles, judge the terrain ahead and adjust its movement in real time.
The researchers tested their robot, called KAIST HOUND, both indoors and outdoors.
It successfully navigated stairs, grassy slopes and paved walkways on the KAIST campus, as well as much more difficult forest trails filled with fallen trees, exposed roots and thick layers of leaves. As it encountered different obstacles, the robot automatically changed between walking, running and jumping without human intervention.
During the outdoor tests, KAIST HOUND reached a top speed of six meters per second, or about 22 kilometers per hour, while maintaining its balance over rugged terrain. The robot was also able to switch between different running styles, including a trot and a bounding motion, depending on the terrain and the speed required.
The researchers believe this new control system represents an important step toward more intelligent walking robots. Instead of following fixed instructions, future robots may be able to make their own movement decisions as they explore unfamiliar environments.
Such technology could prove especially valuable for search-and-rescue missions after natural disasters, military operations in difficult terrain, inspections of industrial facilities and other situations where humans cannot easily or safely go.
Source: KSR.
