研究人员开发了 Any2Any，一种将全身追踪 (WBT) 模型高效迁移到新的人形机器人的方法。该方法显著减少了适应新机器人所需的数据和计算资源，从而能够更快地部署到不同的机器人平台。通过首先对齐运动学特性，然后微调对动力学敏感的模块，Any2Any 允许预训练模型被有效重用，以仅占原始训练成本一小部分的代价实现具有竞争力的性能。 AI
影响 能够更快、更经济高效地将先进的人形机器人控制系统部署到新硬件上。
排序理由 该集群包含两篇研究论文，详细介绍了人形机器人控制和追踪的新方法和模型。
AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →
arXiv:2605.23733v1 Announce Type: cross Abstract: Whole-body tracking (WBT) models have become a key foundation for humanoid robots, enabling them to imitate diverse motions with high fidelity. Training such models from scratch requires large-scale data and computation, making ra…
Whole-body tracking (WBT) models have become a key foundation for humanoid robots, enabling them to imitate diverse motions with high fidelity. Training such models from scratch requires large-scale data and computation, making rapid deployment on new humanoid platforms costly. T…
arXiv:2511.07820v3 Announce Type: replace-cross Abstract: Despite the rise of billion-parameter foundation models trained across thousands of GPUs, similar scaling gains have not been shown for humanoid control. Current neural controllers for humanoids remain modest in size, targ…