用强化学习训练人形机器人踢球、走路、跳跃，提升动作自然性与适应性。
Sim2Real Reinforcement Learning for Soccer skills
- 采用课程学习与对抗运动先验技术提升训练效率
- 在仿真中实现更动态、自适应的踢球等动作控制
- 适合关注机器人技能生成与仿真到现实迁移的研究者
本论文提出一种更高效、有效的强化学习方法，用于训练人形机器人完成控制类任务。传统强化学习方法在适应真实环境、处理复杂性及生成自然动作方面存在局限，而本文通过课程学习与对抗运动先验（AMP）技术克服了这些挑战。结果表明，所训练的踢球、行走和跳跃策略更具动态性和适应性，优于先前方法。然而，从仿真到真实世界的策略迁移失败，凸显当前强化学习方法在完全适应现实场景方面的局限性。
原文摘要 · Abstract (English)
This thesis work presents a more efficient and effective approach to training control-related tasks for humanoid robots using Reinforcement Learning (RL). The traditional RL methods are limited in adapting to real-world environments, complexity, and natural motions, but the proposed approach overcomes these limitations by using curriculum training and Adversarial Motion Priors (AMP) technique. The results show that the developed RL policies for kicking, walking, and jumping are more dynamic, and adaptive, and outperformed previous methods. However, the transfer of the learned policy from simulation to the real world was unsuccessful, highlighting the limitations of current RL methods in fully adapting to real-world scenarios.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。