基于脉冲神经网络的人机协作早期话轮预测
Early Turn-taking Prediction with Spiking Neural Networks for Human Robot Collaboration
摘要：
话轮转换对人类团队合作的结构至关重要。人类通常会意识到团队成员的意图，以保持或放弃他们的回合之前，一个转向开关，其中工作的责任分担任务转移。未来的合作机器人也有望提供这种能力。为此，本文提出了认知话轮转换模型（CTTM），它利用认知模型（即，脉冲神经网络），以实现早期话轮转换预测。CTTM框架可以处理多模态人类交际线索（包括隐式和显式），并在早期阶段预测人类话轮转换意图。所提出的框架进行了测试模拟手术过程中，机器人擦洗护士预测外科医生的轮流的意图。结果发现，建议的CTTM框架优于国家的最先进的话轮转换预测算法的大幅度。当呈现对通信线索的部分观察时，它的表现也优于人类（即，少于40%的完整操作）。这种早期预测能力使机器人能够在早期阶段启动轮流行动，从而促进协作并提高整体效率。
英文摘要：
Turn-taking is essential to the structure of human teamwork. Humans are typically aware of team members' intention to keep or relinquish their turn before a turn switch, where the responsibility of working on a shared task is shifted. Future co-robots are also expected to provide such competence. To that end, this paper proposes the Cognitive Turn-taking Model (CTTM), which leverages cognitive models (i.e., Spiking Neural Network) to achieve early turn-taking prediction. The CTTM framework can process multimodal human communication cues (both implicit and explicit) and predict human turn-taking intentions in an early stage. The proposed framework is tested on a simulated surgical procedure, where a robotic scrub nurse predicts the surgeon's turn-taking intention. It was found that the proposed CTTM framework outperforms the state-of-the-art turn-taking prediction algorithms by a large margin. It also outperforms humans when presented with partial observations of communication cues (i.e., less than 40% of full actions). This early prediction capability enables robots to initiate turn-taking actions at an early stage, which facilitates collaboration and increases overall efficiency.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
