研究人员开发了LadderMan系统，使人形机器人能够爬梯并在爬梯过程中执行任务。该系统采用两阶段学习流程，将多个爬梯专家提炼成统一的基于深度的视觉运动策略。利用视觉基础模型来弥合模拟到现实的深度感知差距，从而实现稳健的现实世界部署。此外，一个独立的操纵策略支持稳定的梯上遥操作。 AI
影响 展示了机器人在复杂、非结构化环境中操纵和导航能力的进步。
排序理由 该集群包含一篇详细介绍新研究系统的学术论文。
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
arXiv:2606.05873v1 Announce Type: cross Abstract: Humanoid robots hold great promise for operating in human-centered environments, yet ladder climbing remains one of the most challenging tasks due to sparse footholds and handholds, complex whole-body coordination, and sensitivity…
Humanoid robots hold great promise for operating in human-centered environments, yet ladder climbing remains one of the most challenging tasks due to sparse footholds and handholds, complex whole-body coordination, and sensitivity to perception and control errors. We present \tex…