Learning Highly Dynamic Skills Transition for Quadruped Jumping Through Constrained Space

2026-08-20

AI Summary

研究通过模仿学习和分层强化学习，使四足机器人学会在狭窄空间中跳跃等高动态技能，解决了四足机器人在受限环境中进行复杂运动的难题。

Abstract

Although legged animals are capable of performing explosive motions while traversing confined spaces, replicating this behavior in quadrupedal robots has been a longstanding challenge. Here, we propose a hierarchical reinforcement learning pipeline that empowers the robots to perform aggressive locomotion through constrained obstacles--a narrow gate. The imitation learning technique is used to train the low-level policy, which mimics the behaviors of real animals and forms a set of diverse skills. The high-level controller, having an awareness of the capability of low-level skills and acquiring the gate information via vision-based detection, determines the suitable maneuvers with collision-free trajectories to traverse it dynamically. Notably, we also verify that this framework can be extended to other highly dynamic tasks. This is one of the first works that perform autonomous and agile aerial gate traversal tasks on ground-walking robots, extending the lifelike agility of legged robots to match that of their biological counterparts.

Problem

• Quadruped Robots
• Dynamic Skills
• Constrained Space

Innovation

• Hierarchical Reinforcement Learning
• Imitation Learning
• Dynamic Locomotion
• Vision-based Detection
• Collision-free Trajectories