HINT-Blimp：基于多模态线索的机器人飞艇人类意图推断
HINT-Blimp: Human INTent Inference from Multimodal Cues for Robotic Blimps
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- Lehigh University（理海大学）
- Swarthmore College（斯沃斯莫尔学院）
- University of Pennsylvania（宾夕法尼亚大学）
机构由 AI 辅助整理，请以论文原文为准。
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中文总结 AI 辅助
本文提出一种基于多模态稀疏信号（推力与语音）的人机交互框架，利用参数化线性动态系统和粒子滤波在线推断人类意图，并在机器人飞艇上验证，300次试验中86%在五次交互内识别目标，并能生成避障轨迹。
中文摘要 AI 辅助
在人机交互中，操纵杆和手持平板等传统界面会给导航任务带来延迟，并要求操作者将注意力明确放在设备上，而非机器人身上。我们提出了一种新的人机交互框架，在该框架中，人类通过物理推力和口头命令等稀疏多模态信号直接传达意图。人类意图被表示为参数化的线性动态系统（LDS），该系统编码了期望的目标和运动行为。机器人使用粒子滤波器在线估计该意图（参数），其中每个粒子代表一个候选的LDS假设，并随着新信息的可用而在线重新加权。我们在机器人飞艇上验证了这一框架，飞艇固有的柔顺性和碰撞容忍性使其非常适合重复的物理交互。多名参与者参与的300次试验实验表明，结合推力和语音命令，在最多五次交互内，86%的试验中识别出了预期目标，且大多数试验在两次交互内解决。推断出的动态系统还能产生弯曲轨迹，以避开仅人类已知的障碍物。
英文摘要
In human-robot interaction, traditional interfaces such as joysticks and handheld tablets introduce latency into navigation tasks and require the operator's explicit attention on the device, instead of the robot. We propose a new human-robot interaction framework in which a human communicates intent directly through sparse multimodal signals such as physical pushes and spoken commands. Human intent is represented as a parameterized linear dynamical system (LDS) that encodes the desired goal and motion behavior. The robot estimates this intent (parameters) online using a particle filter, where each particle represents a candidate LDS hypothesis and is reweighted online as new information becomes available. We validate this framework on a robotic blimp, whose inherent compliance and collision tolerance make it well-suited for repeated physical interaction. Experiments with multiple participants across 300 trials show that combining pushes and voice commands identifies the intended goal in 86% of trials within at most five interactions, with most trials resolved in two. The inferred dynamical systems can also produce curved trajectories that avoid obstacles known only to the human.
