发表机构
National Cheng Kung University（国立成功大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对人形机器人获取复杂技能的挑战，提出利用生成式AI将文本提示转换为多样人体运动序列的框架，用作训练资源，使机器人无需人工干预就能学习多种任务执行风格，实验验证了该方法的有效性和适应性。
AI 中文摘要
人形机器人类人的形态赋予它们在敏捷和多功能运动能力方面的巨大潜力，但在获取复杂技能时也带来了重大挑战。传统的示范学习方法往往受到收集真实世界数据成本高、捕捉特定运动行为困难以及示范个体间多样性有限的限制。此外，即使对于同一任务，人类也可能以多种不同方式执行动作。本文提出了一个新框架，利用生成式人工智能的力量将文本提示转换为逼真且多样的人体运动序列，使机器人能够观察到单个任务的多种执行方式。这些合成示范随后用作训练资源，使机器人无需直接人工干预就能学习广泛的任务执行风格。我们在四个模拟场景中评估了所提出的方法。实验结果表明，机器人不仅成功完成任务，还表现出对运动复杂变化的强大适应性。
英文摘要
The human-like morphology of humanoid robots grants them exceptional potential for agile and versatile motor capabilities, but it also introduces significant challenges in acquiring complex skills. Traditional Learning-from-Demonstrations methods are often constrained by the high cost of collecting real-world data, the difficulty of capturing motion-specific behaviors, and the limited diversity of demonstrations across individuals. Moreover, even for the same task, humans may execute the motion in multiple distinct ways. In this paper, we propose a new framework that leverages the power of Generative AI to convert textual prompts into realistic and diverse sequences of human body movements, enabling the robot to observe multiple variations of how a single task can be performed. These synthetic demonstrations are then used as a training resource, allowing the robot to learn a broad range of task-execution styles without requiring direct human intervention. We evaluate the proposed method across four simulation scenarios. Experimental results show that the robot not only completes the tasks successfully but also demonstrates strong adaptability to complex variations in motion.
CommentsAccepted to the 2026 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)
