English(EN) Learning Manipulation-Sufficient Representations via Outcome Bottlenecks

机器人学研究引入基于结果的表示学习

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-15 04:00

研究人员开发了一种在机器人学中学习操纵充分表示的新方法，该方法侧重于动作结果而非密集几何状态。该方法利用了带有KL正则化项的动作条件结果瓶颈，旨在保留可接受动作的结果分布。该系统在抓取选择和主动视点选择等各种任务中都显示出有效性，与重建几何方法相比，抓取成功率的AUC显著提高。 AI

影响这项研究通过优化动作结果的表示，有望实现更高效、更有效的机器人操纵系统。

排序理由该条目是一篇学术论文，详细介绍了一种机器人学中的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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arXiv cs.CV TIER_1 English(EN) · Md Selim Sarowar, Sungho Kim · 2026-09-15 04:00

通过结果瓶颈学习操纵充分表示

arXiv:2609.13235v1 Announce Type: cross Abstract: Networked manipulation endpoints couple perception to actuation across compute- and bandwidth-limited links, yet commonly exchange dense geometric states optimized for fidelity rather than action outcomes. A stochastic representat…