条件付き敵対的モーションプライアを用いた多技能脚式移動の学習
Learning Multi-Skill Legged Locomotion Using Conditional Adversarial Motion Priors
専門家のデモンストレーションから四足歩行ロボットが複数の移動技能を単一のポリシーで獲得し、滑らかに切り替えられるようにする条件付き敵対的モーションプライア（CAMP）ベースの学習フレームワークを提案した。
著者: Ning Huang, Zhentao Xie, Qinchuan Li
分類: cs.RO
原文アブストラクト
Despite growing interest in developing legged robots that emulate biological locomotion for agile navigation of complex environments, acquiring a diverse repertoire of skills remains a fundamental challenge in robotics. Existing methods can learn motion behaviors from expert data, but they often fail to acquire multiple locomotion skills through a single policy and lack smooth skill transitions. We propose a multi-skill learning framework based on Conditional Adversarial Motion Priors (CAMP), with the aim of enabling quadruped robots to efficiently acquire a diverse set of locomotion skills from expert demonstrations. Precise skill reconstruction is achieved through a novel skill discriminator and skill-conditioned reward design. The overall framework supports the active control and reuse of multiple skills, providing a practical solution for learning generalizable policies in complex environments.