研究人员开发了CoorDex，这是一个新颖的学习流程，使人形机器人在运动中能够执行灵巧的操作。该系统将高维身体和手部控制转换为协调的潜在残差控制，使像Unitree G1这样的人形机器人能够在不停顿的情况下抓取瓶子、打开冰箱门和操纵方块。该方法利用模拟演示来训练运动跟踪教师，将它们提炼成潜在的先验知识，然后使用这些先验知识进行强化学习，在复杂的同时运动和操纵任务中表现优于更简单的方法。 AI
影响 人形机器人操作能力的进步，使其能够执行更复杂的现实世界任务和交互。
排序理由 该集群包含多篇详细介绍人形机器人操作新方法和数据集的研究论文。
AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →
完整方法见我们的编辑标准。
Humanoid loco-manipulation is often simplified into a stop-and-go process: walking to an object, stopping to manipulate it, and then resuming locomotion. It also commonly relies on low degree-of-freedom (DoF) end effectors that behave like an open-close grasp primitive. We introd…
arXiv:2510.08807v2 Announce Type: replace-cross Abstract: From loco-motion to dextrous manipulation, humanoid robots have made remarkable strides in demonstrating complex full-body capabilities. However, the majority of current robot learning datasets and benchmarks mainly focus …
arXiv:2509.26633v3 Announce Type: replace-cross Abstract: A dominant paradigm for teaching humanoid robots complex skills is to retarget human motions as kinematic references to train reinforcement learning (RL) policies. However, existing retargeting pipelines often struggle wit…