Built independently by an author, for readers. Read the story and support ChapterPal
ChapterPal Logo
Log inSign up

keyword

quadrupedal locomotion

Quadrupedal locomotion is a form of terrestrial movement in which an animal or robotic system uses four limbs to support its body weight and propel itself across a surface. Characterized by various coordinated gait cycles, such as walking, trotting, pacing, and galloping, this type of locomotion relies on sequenced limb movements and dynamic weight distribution to maintain balance. Because it utilizes discrete footholds rather than continuous rolling contact, quadrupedal locomotion provides exceptional stability and agility over rough, irregular, or obstacle-laden terrain. In both biological studies and engineering, it serves as a foundational framework for understanding multi-limb coordination and for developing legged robots capable of navigating complex environments.

1 item

Learning quadrupedal locomotion over challenging terrain

Learning quadrupedal locomotion over challenging terrain

Joonho Lee, Jemin Hwangbo, Lorenz Wellhausen, Vladlen Koltun, Marco Hutter

OrganizationsETH ZurichIntelKorea Advanced Institute of Science and Technology

Why you should read this

Demonstrates that a reinforcement learning controller trained purely in simulation using proprioceptive feedback achieves zero-shot transfer to traverse extreme, unmodeled natural terrains such as mud, snow, and dynamic rubble on physical quadruped robots.

Some of the most challenging environments on our planet are accessible to quadrupedal animals but remain out of reach for autonomous machines. Legged locomotion can dramatically expand the operational domains of robotics. However, conventional controllers for legged locomotion are based on elaborate state machines that explicitly trigger the execution of motion primitives and reflexes. These designs have escalated in complexity while falling short of the generality and robustness of animal locomotion. Here we present a radically robust controller for legged locomotion in challenging natural environments. We present a novel solution to incorporating proprioceptive feedback in locomotion control and demonstrate remarkable zero-shot generalization from simulation to natural environments. The controller is trained by reinforcement learning in simulation. It is based on a neural network that acts on a stream of proprioceptive signals. The trained controller has taken two generations of quadrupedal ANYmal robots to a variety of natural environments that are beyond the reach of prior published work in legged locomotion. The controller retains its robustness under conditions that have never been encountered during training: deformable terrain such as mud and snow, dynamic footholds such as rubble, and overground impediments such as thick vegetation and gushing water. The presented work opens new frontiers for robotics and indicates that radical robustness in natural environments can be achieved by training in much simpler domains.

Added

2026-09-24