Four-Legged Robot Navigates Complex Terrains with Ease

Innovations in robotics are allowing machines to navigate intricate environments just like living organisms. A recent breakthrough in Hong Kong demonstrates how quadrupedal robots can autonomously traverse obstacles ranging from urban curbs to rugged landscapes.

By Michelle Hampson · 2025-09-24 19:00:00 · From IEEE Spectrum via spectrum.ieee.org

Flexible navigation in robots has become a pressing concern as their potential applications grow. From disaster recovery to reconnaissance missions, having machines that can adapt to varying types of terrain presents numerous opportunities and challenges.

Core Idea/Problem

The central challenge in robot navigation lies in addressing the varying complexities of real-world environments. Animals, both two- and four-legged, can seamlessly adapt to different terrains. For example, a dog can climb over a fence or dash under a table without hesitation. Researchers are now striving to create robots that mimic this versatility. Specifically, quadrupedal robots need to effectively map their surroundings and tackle obstacles like gaps to jump over or hazards to crawl under. This need for agility and adaptability opens up a range of scenarios where robots could take on tasks that may be too dangerous for humans, such as navigating rubble after natural disasters.

What the Data/Details Show

A team in Hong Kong, led by Peng Lu, an assistant professor at the University of Hong Kong, focused on developing a robot capable of overcoming various obstacles through advanced mapping techniques. Their breakthrough involves creating a multilayer elevation map using sensor data, particularly from lidar technology. This allows robots to better understand and navigate a range of terrains.

The research was detailed in a study published on August 4 in the IEEE Robotics and Automation Letters. They utilized simulations to familiarize the robot with different terrains and obstacles it might face, such as jumping across gaps or crawling beneath overhanging objects. If sensor data is missing, the robot can still estimate conditions based on previously acquired training data.

“Through learning different skills in simulation and knowledge distillation, the robot is able to switch among different skills to traverse through different obstacles,” says Lu.

The team tested their mapping model using the Unitree Go1 robot, which demonstrated its ability to negotiate various obstacles autonomously. This included modes where the robot would switch seamlessly between crawling, jumping, and climbing as needed.

How It Works / The Mechanism

The robot’s functionality lies in its ability to generate a multilayer elevation map, informed by lidar data and real-time sensor readings. This mapping enables it to analyze its surroundings in detail, reflecting features like height changes and obstacle placement. The simulation-based training approach equips the robot with diverse skills, enabling it to adapt dynamically to different obstacles. Importantly, this method allows the robot to exhibit semi-autonomous pathfinding capabilities; in instances where it encounters a barrier too high to cross, it can reroute itself by finding a way around through trial and error.

Implications & Use Cases

One of the most promising applications of this technology is in post-disaster scenarios, where the ability to navigate unstable rubble can be vital for search and rescue operations. In construction, these robots could serve as inspectors, autonomously assessing sites that may be too dangerous for human workers. The versatility of this technology also opens the door to urban navigation, with potential implementations in delivery services or even military reconnaissance.

Limitations, Caveats & Unknowns

While the robot’s ability to navigate complex terrains is impressive, it does come with constraints. The current model relies exclusively on pre-trained data and is unable to learn or adapt from real-world encounters. This limitation presents challenges when faced with entirely new terrain types or unencountered obstacles. Researchers need to bridge this gap by developing mechanisms that allow the robot to learn on the fly from real-world data.

What’s Next

Looking ahead, Lu mentioned potential plans for commercializing this robot technology for inspection scenarios, particularly in environments like construction sites where real-world data could be employed for further improvements. The pace of development suggests that enhancements may allow for more efficient real-time learning, further expanding the robot’s capability to handle diverse terrains.

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