论文/arXiv 2609.40137·28 天前

通过自博弈进行游戏引导的技能发现以实现可玩智能体控制

GGSD用竞技游戏自博弈为具身智能体发现人类可玩的离散运动技能，并在Ant、Franka-arm和Unitree G1环境验证。

AI 整理

• 硬件零部件
• 本体 SDK 与控制

宇树 G1Franka FR3

原文链接：Game-Guided Skill Discovery through Self-Play for Playable Agent Control（arXiv 2609.40137 · 发布日期：2026-09-30）

Game-Guided Skill Discovery through Self-Play for Playable Agent Control

GGSD用竞技游戏自博弈为具身智能体发现人类可玩的离散运动技能，并在Ant、Franka-arm和Unitree G1环境验证。

核心要点

• 动机是用少量学习行为替代低层动作控制，同时获得语义区分、可解释且表达力强的技能。
• 方法让层级智能体与过去的自己竞争，高层选择小型离散技能集，低层学习技能条件化行为。
• 训练后人类可替代高层策略，通过同一离散技能直接控制智能体，并由技能切换产生组合行为。
• 在Ant、Franka-arm和Unitree G1环境中，人类可组合技能解决Maze、CubePush等未见任务；论文提供交互式演示。

验证范围与局限：验证范围为Ant、Franka-arm和Unitree G1环境，以及Maze、CubePush等未见任务。

英文摘要 (Abstract)

We present Game-Guided Skill Discovery (GGSD), a framework that uses self-play in games to discover motor skills that are directly playable by humans. Playable skills provide a compact abstraction for controlling embodied agents through a small set of learned behaviors rather than low-level actions. To be effective, these skills should be semantically distinct, interpretable, and expressive; properties that existing unsupervised skill-discovery methods often fail to achieve simultaneously. GGSD achieves these desiderata by grounding skill discovery in competitive gameplay. A hierarchical agent competes against its past selves, with a high-level policy selecting from a small discrete skill set and a skill-conditioned low-level policy learning the corresponding behaviors. After training, a human can replace the high-level policy and directly control the agent through the same discrete skills. Despite the small number of high-level actions, skill transitions give rise to emergent combo behaviors, expanding expressivity beyond individual primitives. Across Ant, Franka-arm, and Unitree G1 environments, we show that GGSD produces human-playable skills that humans can compose to solve unseen tasks, such as Maze and CubePush, without additional training. An interactive demo is available at https://ggsd-demo.github.io.

作者：Seungeun Rho, Jeonghwan Kim, Xue Bin Peng, Sehoon Ha

来源

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1. arXiv 2609.40137：Game-Guided Skill Discovery through Self-Play for Playable Agent Control访问 2026-10-21