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cs.RO updates on arXiv.org

FineCog-Nav: Integrating Fine-grained Cognitive Modules for Zero-shot Multimodal UAV Navigation DENALI: A Dataset Enabling Non-Line-of-Sight Spatial Reasoning with Low-Cost LiDARs SENSE: Stereo OpEN Vocabulary SEmantic Segmentation Continual Hand-Eye Calibration for Open-world Robotic Manipulation PLAF: Pixel-wise Language-Aligned Feature Extraction for Efficient 3D Scene Understanding GaussianFlow SLAM: Monocular Gaussian Splatting SLAM Guided by GaussianFlow GIST: Multimodal Knowledge Extraction and Spatial Grounding via Intelligent Semantic Topology $π_{0.7}$: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities R3D: Revisiting 3D Policy Learning Vision-Based Safe Human-Robot Collaboration with Uncertainty Guarantees Benchmarking Classical Coverage Path Planning Heuristics on Irregular Hexagonal Grids for Maritime Coverage Scenarios NEAT-NC: NEAT guided Navigation Cells for Robot Path Planning HRDexDB: A Large-Scale Dataset of Dexterous Human and Robotic Hand Grasps ADAPT: Benchmarking Commonsense Planning under Unspecified Affordance Constraints An Intelligent Robotic and Bio-Digestor Framework for Smart Waste Management Efficient closed-form approaches for pose estimation using Sylvester forms World-Value-Action Model: Implicit Planning for Vision-Language-Action Systems A Nonasymptotic Theory of Gain-Dependent Error Dynamics in Behavior Cloning CooperDrive: Enhancing Driving Decisions Through Cooperative Perception SpaceMind: A Modular and Self-Evolving Embodied Vision-Language Agent Framework for Autonomous On-orbit Servicing HiVLA: A Visual-Grounded-Centric Hierarchical Embodied Manipulation System UMI-3D: Extending Universal Manipulation Interface from Vision-Limited to 3D Spatial Perception Towards Multi-Object-Tracking with Radar on a Fast Moving Vehicle: On the Potential of Processing Radar in the Frequency Domain Beyond Conservative Automated Driving in Multi-Agent Scenarios via Coupled Model Predictive Control and Deep Reinforcement Learning Failure Identification in Imitation Learning Via Statistical and Semantic Filtering A Dynamic-Growing Fuzzy-Neuro Controller, Application to a 3PSP Parallel Robot Vision-Language-Action Jump-Starting for Reinforcement Learning Robotic Agents A Mechanistic Analysis of Sim-and-Real Co-Training in Generative Robot Policies ESCAPE: Episodic Spatial Memory and Adaptive Execution Policy for Long-Horizon Mobile Manipulation Evolvable Embodied Agent for Robotic Manipulation via Long Short-Term Reflection and Optimization
A Service Robot's Guide to Interacting with Busy Customers
Suraj Nukala, Meera Sushma, Leimin Tian, Akansel Cosgun, Dana Ku · 2025-12-19 · via cs.RO updates on arXiv.org

The growing use of service robots in hospitality highlights the need to understand how to effectively communicate with pre-occupied customers. This study investigates the efficacy of commonly used communication modalities by service robots, namely, acoustic/speech, visual display, and micromotion gestures in capturing attention and communicating intention with a user in a simulated restaurant scenario. We conducted a two-part user study (N=24) using a Temi robot to simulate delivery tasks, with participants engaged in a typing game (MonkeyType) to emulate a state of busyness. The participants' engagement in the typing game is measured by words per minute (WPM) and typing accuracy. In Part 1, we compared non-verbal acoustic cue versus baseline conditions to assess attention capture during a single-cup delivery task. In Part 2, we evaluated the effectiveness of speech, visual display, micromotion and their multimodal combination in conveying specific intentions (correct cup selection) during a two-cup delivery task. The results indicate that, while speech is highly effective in capturing attention, it is less successful in clearly communicating intention. Participants rated visual as the most effective modality for intention clarity, followed by speech, with micromotion being the lowest ranked.These findings provide insights into optimizing communication strategies for service robots, highlighting the distinct roles of attention capture and intention communication in enhancing user experience in dynamic hospitality settings.