Evaluation of an Actuated Spine in Agile Quadruped Locomotion

2026-05-08

AI Summary

This work proposes a sagittal-plane, single-degree-of-freedom actively actuated biomimetic spine to enhance the agility of quadrupedal robots in complex scenarios such as high-speed running, stair climbing, obstacle traversal, and navigation through narrow passages. The design is integrated into MAB Robotics’ Silver Badger platform and trained using reinforcement learning within the MuJoCo simulation environment. Systematic evaluation demonstrates that the active spine significantly improves locomotion performance across multiple metrics, including maximum traversable obstacle height, adaptability to steep inclines, and passage negotiation capability. Experimental results show that the robot equipped with the active spine consistently outperforms the spineless baseline in various high-dynamic tasks, achieving markedly enhanced overall agility.

Abstract

The spine plays a crucial role in the dynamic locomotion of quadrupedal animals, improving the stability, speed, and efficiency of their gait, especially for fast-paced and highly agile movements. Therefore, the spine is also a promising and natural way to extend the capabilities of quadruped robots. This paper empirically investigates the benefits of an actuated spine for learning agile quadruped locomotion. We evaluate whether the use of the spine brings benefits in terms of high-speed running, climbing stairs, climbing high-angle slopes, hurdling, and crawling scenarios. We conducted an empirical study in MuJoCo simulation using the Silver Badger robot from MAB Robotics with an actuated 1-DOF spine in the sagittal plane. The obtained results show that the use of the spine provides the robot with increased agility and allows it to overcome higher stairs, steeper slopes, higher obstacles, and smaller passages.

Problem

• actuated spine
• quadruped locomotion
• agility
• dynamic movement
• robotic mobility

Innovation

• actuated spine
• quadruped locomotion
• agile robotics
• empirical evaluation
• MuJoCo simulation