Ask Before It Tells: Benchmark-to-Robot Body-Cue Transfer for a Question-First Bedside Robot

Service robots in retail stores increasingly rely on cascaded speech pipelines (STT-LLM-TTS), yet many customer-robot interactions are initiated or guided by nonverbal behaviors such as approaching, waving, pointing, or showing items. This paper studies such cues in a real-world store deployment with a teleoperated humanoid robot and shows that a non-negligible portion of robot turns are triggered by nonverbal behaviors rather than spoken input, revealing a limitation of audio-only dialogue systems. In a 6-day in-the-wild deployment, 15.3% of robot utterances were initiated by users' nonverbal behaviors rather than spoken input. Based on an analysis of observed customer behaviors, we define a set of frequent, service-relevant nonverbal cues and develop a real-time multi-person, multi-label recognizer that runs online from video. We then propose a dialogue framework that conditions LLM-based utterance generation on recognized nonverbal cue tokens, and optionally leverages a vision-language model when items are shown, enabling proactive robot responses without hand-crafted rules. We evaluate the approach offline on nonverbal-triggered turns and demonstrate an online prototype that reacts to users' nonverbal cues in real time.

Robots that follow natural-language instructions in everyday indoor environments must act on incomplete human utterances. Instructions often omit essential information, such as the identity of an out-of-view object, an intended destination, or the user's goal. Existing datasets contain little real-world situated dialogue and provide few practice-grounded criteria for deciding when a robot should act, confirm, clarify, or refuse. We retrospectively analyze a pilot Wizard-of-Oz study in which five participants performed everyday indoor tasks, including door opening, drawer opening, feeding, drinking, and cleaning, with a wheelchair-mounted mobile manipulator while the wizard responded without a formal communication policy. This preserved authentic user behavior but produced inconsistent robot-side decisions, motivating an explicit decision scheme. From 40 episodes, we derived a hierarchical taxonomy of six response modes (ANSWER, REPORT_DONE, REFUSE, CONFIRM, CLARIFY, ACT) and four ambiguity types (intent, referential, spatial, intelligibility). Two human annotators and an AI annotator applied the scheme to the pilot data. Clean-label rates were 91% and 89%, and Cohen's ranged from 0.72 to 0.95 across decision-point, mode, and ambiguity levels for both human-human and human-AI comparisons. Fine-tuning LLaVA-1.6-7B on taxonomy-derived labels for ACT and CLARIFY indicates the feasibility of training vision-language models using annotations from our taxonomy. Remaining boundary cases in decision-point identification and REPORT_DONE motivate a constrained protocol for more consistent dialogue collection.

Effective robot-to-human communication can increase transparency and trust, reduce uncertainty, and contribute to safer collaboration in shared workspaces. Designing and validating an effective robot communication strategy is challenging due to the varying and often limited communication modalities across robots, differences in how diverse recipients interpret messages, and the underexplored virtual-to-real gap in studies of communication legibility. We present a systematic, large-scale comparative validation of existing communication strategies for a mobile non-humanoid robot across message types and settings (online and in-person). Based on the prescribed message types in the existing standards for industrial robots, we realize and compare a low-expressive, unimodal LED-based strategy with a highly expressive, multimodal one that leverages robotic gaze, gestures, and voice. For each strategy, we analyze the communication of a turning intention, an attention request, error status, whether the robot is stuck, and whether it is functioning normally. We evaluate these strategies in replicated online and in-person experiments. We find strong evidence that highly expressive multimodal communication is perceived as more legible and intuitive than unimodal LED-based communication. Comparing the online and real-world study findings, we observe a notable decrease in overall legibility, particularly for signaling with LEDs. Similarly, confidence in message interpretation decreases during the real-world evaluation.