HINT-Blimp: Human INTent Inference from Multimodal Cues for Robotic Blimps

2026-09-22

AI Summary

为解决人机交互中传统接口的延迟问题，提出HINT-Blimp框架，通过物理推动和语音命令等多模态信号直接传达意图，并用粒子滤波在线估计意图。

Abstract

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.

Problem

• human-robot interaction
• multimodal cues
• intent inference

Innovation

• Human-robot interaction
• Multimodal signals
• Parameterized linear dynamical system
• Particle filter
• Robotic blimp