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新PRISM框架通过推断人类互动风格来增强机器人导航能力

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了PRISM，一个旨在改善拥挤环境中社交机器人导航能力的新框架。PRISM通过被动观察人与人之间的互动来推断人类的互动特征，并使用经过Rank-N-Contrast损失训练的Transformer编码器将这些轨迹编码到潜在空间中。这种方法旨在考虑通常被仅基于几何的导航系统所忽略的互动倾向的个体差异。在模拟中，与现有方法相比，PRISM在降低碰撞率方面表现出色，并在导航时间和路径长度方面有所改进。 AI

影响 通过结合人类互动风格预测来增强社交环境中的机器人导航能力。

排序理由 这是一篇详细介绍机器人导航新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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

新PRISM框架通过推断人类互动风格来增强机器人导航能力

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这是一篇详细介绍机器人导航新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Bo-Han Chen, Hiromu Taketsugu, Norimichi Ukita ·

    PRISM：用于社交机器人导航的交互风格和运动的预测表示

    arXiv:2609.18125v1 Announce Type: new Abstract: Humans often observe others before interacting and adjust their behavior accordingly. Robot navigation in crowds, however, often represents pedestrians mainly by observed geometric states, leaving individual differences in interacti…

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