新的PACE方法利用本体感觉增强机器人仿真到现实的迁移

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-10 04:00

研究人员开发了一种名为PACE（本体感觉锚定的跨模态编码器）的新方法，以改进强化学习策略从仿真到现实机器人任务的迁移。PACE利用在仿真和硬件中保持一致的本体感觉数据来锚定视觉和力/扭矩表示。这种方法有助于模型抑制特定于域的视觉变化，并专注于与任务相关的运动线索。在四个接触式装配任务上部署时，使用PACE训练的策略在现实世界中达到了93.3%的成功率，sim-to-real迁移损失极小，优于基线方法。 AI

影响增强机器人装配任务的仿真到现实迁移能力，可能加速AI训练机器人在现实世界的部署。

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Yuhan Wang, Yurou Chen, Hongye Jiang, Wenzhao Lian · 2026-09-10 04:00

零样本模拟到现实的接触式装配，通过本体感觉锚定的跨模态预训练

arXiv:2609.07534v1 Announce Type: cross Abstract: Contact-rich assembly remains challenging because it requires submillimeter spatial accuracy and reliable interpretation of forces during sustained contact. Although simulation-based reinforcement learning offers a scalable traini…

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零样本模拟到现实的接触式装配，通过本体感觉锚定的跨模态预训练

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