两篇新的研究论文介绍了用于通过自然语言控制基于物理的人形的高级扩散模型。SCRIPT 采用多阶段训练框架，结合了联合动作-状态-文本扩散 Transformer 和具有混合奖励的强化学习，以提高指令遵循和运动质量。MIND 采用多尺度意图扩散机制，使用行为意图作为文本命令和低级动作之间的桥梁，并展示了改进的语义对齐和物理合理性。 AI
影响 这些模型代表了具身智能的进步，有可能通过自然语言指令实现更复杂、更直观的机器人控制。
排序理由 arXiv 上发表的两篇学术论文介绍了控制基于物理的人形的新方法。
AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →
arXiv:2605.22894v1 Announce Type: cross Abstract: Controlling physics-based humanoids from natural-language instructions is a critical step toward general-purpose embodied agents. However, existing methods remain constrained by a tension between semantic expressiveness and physic…
arXiv:2605.26006v1 Announce Type: new Abstract: Enabling physics-based humanoids to execute diverse behaviors from high-level textual commands remains a significant challenge. Existing methods typically follow either a two-stage paradigm that combines kinematic motion generation …
Enabling physics-based humanoids to execute diverse behaviors from high-level textual commands remains a significant challenge. Existing methods typically follow either a two-stage paradigm that combines kinematic motion generation with physics-based tracking, or an end-to-end im…