GROVE: A Generalized Reward for Learning Open-Vocabulary Physical Skill

• URL: http://arxiv.org/abs/2504.04191v1
• Date: Sat, 05 Apr 2025 14:44:47 GMT
• Title: GROVE: A Generalized Reward for Learning Open-Vocabulary Physical Skill
• Authors: Jieming Cui, Tengyu Liu, Ziyu Meng, Jiale Yu, Ran Song, Wei Zhang, Yixin Zhu, Siyuan Huang,
• Abstract summary: Learning open-vocabulary physical skills for simulated agents presents a significant challenge in artificial intelligence.<n>We introduce GROVE, a generalized reward framework that enables open-vocabulary physical skill learning without manual engineering or task-specific demonstrations.<n>To bridge the domain gap between simulation and natural images, we develop Pose2CLIP, a lightweight mapper that efficiently projects agent poses directly into semantic feature space.
• Score: 44.95563610228887
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Learning open-vocabulary physical skills for simulated agents presents a significant challenge in artificial intelligence. Current reinforcement learning approaches face critical limitations: manually designed rewards lack scalability across diverse tasks, while demonstration-based methods struggle to generalize beyond their training distribution. We introduce GROVE, a generalized reward framework that enables open-vocabulary physical skill learning without manual engineering or task-specific demonstrations. Our key insight is that Large Language Models(LLMs) and Vision Language Models(VLMs) provide complementary guidance -- LLMs generate precise physical constraints capturing task requirements, while VLMs evaluate motion semantics and naturalness. Through an iterative design process, VLM-based feedback continuously refines LLM-generated constraints, creating a self-improving reward system. To bridge the domain gap between simulation and natural images, we develop Pose2CLIP, a lightweight mapper that efficiently projects agent poses directly into semantic feature space without computationally expensive rendering. Extensive experiments across diverse embodiments and learning paradigms demonstrate GROVE's effectiveness, achieving 22.2% higher motion naturalness and 25.7% better task completion scores while training 8.4x faster than previous methods. These results establish a new foundation for scalable physical skill acquisition in simulated environments.

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