Social Robot Interaction and HRI

Neueste Paper

• TANGO: Humanoid Navigation in Cluttered Environments with a Whole-Body Vision-Language-Action Model

  Anqi Li, Yuxin Chen, Zhaobo Li, Zhuo Cao, Junli Ren, Masayoshi Tomizuka, Dhruv Shah · 2. Oktober 2026

  We study the problem of navigating cluttered indoor environments with a humanoid robot. Unlike conventional methods that model navigation as a 2D path planning problem, humanoid traversal in cluttered environments requires continuous geometry-aware whole-body adaptation, including coordinated arm pl…

• DuoMind: Enabling Distributed Multi-Robot Coordination with Semantic Communication

  Hanchu Zhou, Dechen Gao, Hang Wang, Brendan Lynch, Boqi Zhao, Qiyao Ma, Raman Goyal, Junshan Zhang · 2. Oktober 2026

  Vision-language models (VLMs) and vision-language-action models (VLAs) have recently driven rapid progress in general-purpose robots, yet most progress has focused on single-robot settings. Extending these capabilities to multi-robot systems remains challenging because robots must coordinate long-ho…

• Toward a Culturally Adapted Chinese Language Agent: A Wizard-of-Oz Study of Nonverbal Behavior in Chinese-German Intercultural Interaction

  Siddhant Jain, Anna Lea Reinwarth, Dimitra Tsovaltzi, Rafael Math, Julia Renner · 1. Oktober 2026

  Successful intercultural communication requires more than grammatical competence. It demands sensitivity to culturally embedded social norms whose violation triggers subtle but meaningful nonverbal responses. For German learners of Mandarin Chinese, acquiring this sensitivity is critical yet poorly …

• AnthroDial: Benchmarking LLM Anthropomorphism in Autonomous Social Interaction

  Wentao Liu, Xi Chen, Siyu Song, Biao Yuan, Yu Zhang, Zhou Zhuotong, Jingying Zhou, Guohao Feng, Shasha Hu, Tianfu Wang, Shangshang Yang, Haoyang Liu, Youjia Li, Xiaokun Wang, Min Ji, Ji Wang · 1. Oktober 2026

  Large language models (LLMs) are increasingly deployed as social agents, yet credible human-like interaction requires more than fluent responses or persona consistency. Agents must autonomously decide whether, when, and how to communicate while adapting to evolving contexts, goals, and relationships…

• DexHoldem: An Agentic Robotics Benchmark for Dexterous Manipulation in Texas Hold'em

  Feng Chen, Tianzhe Chu, Li Sun, Pei Zhou, Zhuxiu Xu, Shenghua Gao, Yuexiang Zhai, Yanchao Yang, Yi Ma · 1. Oktober 2026

  Evaluating embodied systems with real dexterous hardware requires more than isolated motor-skill tests: an agent must perceive a changing scene (e.g. a tabletop), choose a context-appropriate action, execute it with a dexterous hand, and leave the scene usable for later decisions. We introduce DexHo…

• GestAdapt: Workspace-Conditioned Co-Speech Gesture Generation for Humanoid Robots

  Bosong Ding, Xianglin Zhang, Miao Xin, Murat Kirtay, Giacomo Spigler · 1. Oktober 2026

  Co-speech gestures for robots must adapt not only to speech and embodiment, but also to the workspace available for performing the motion. Since the same speech can be accompanied by different gestures, a robot can respond to workspace constraints, e.g., gestures for speech next to a wall. In these …

• Benevolent Bias in Multi-Turn Human-Agent Dialogue

  Qianqi Liu, Jin Huang, Fethiye Irmak Dogan, Hatice Gunes · 30. September 2026

  Bias in human-agent interaction can manifest not only through hostile language but also as benevolent bias, whereby unequal treatment hides behind a warm, positive tone. To make it detectable, we operationalise benevolent bias along two dimensions, tone and treatment, yielding three classes: neutral…

• Beyond Tasks: A Vision for Reproducing an Animal-like Behavioral Substrate Using Modern Robot Learning Techniques

  Samiyuru Menik, Hemadri Jayalath · 29. September 2026

  Recent advances in robot learning have produced increasingly capable embodied agents. Yet comparatively less attention has been given to a more basic form of competence that animals exhibit continuously: the ability to remain situated, responsive, and behaviorally coherent as physical, environmental…

• Robots That Take Initiative: A Framework for Building and Evaluating Proactive Robots

  Maithili Patel, Sonia Chernova · 25. September 2026

  Effective robot assistance beyond narrow roles and repetitive tasks requires robots to be proactive - to decide what needs to be done rather than waiting to be told. While proactivity is increasingly explored, it lacks a unified formulation, and work in the domain is typically evaluated offline agai…

• Toward User-Mediated Self-Repair in Ubiquitous Robots Through Goal-Oriented Agentic AI

  Morten Roed Frederiksen · 23. September 2026

  Ubiquitous robotic systems often lack traditional visual interfaces, necessitating resilient natural language interaction for maintenance and repair tasks. This paper presents a goal oriented agentic AI architecture designed to enable non-expert users to perform technical repairs through situated di…

• Cognitive Action Reasoning for Proactive Robots from Human-Centered Multimodal Observations

  Zhihao Gu, Kechao Zhu, Yuanfeng Wu, Mohan Liu, Ankit Kumar Shaw, ChenDong Hong, Xuanyu Chen, Dengchen Mei, Xu Tianyi, Lin Wang · 22. September 2026

  Robots operating in human-centered environments are typically designed to execute explicit instructions, and most robot-learning datasets likewise pair observations with task instructions or low-level actions. Although recent work has begun to explore proactive embodied assistance, existing resource…

• RobotEQ-Video: A Video-Centric Benchmark for Social Proactive Intelligence with World-State Taxonomy

  Xinyi Che, Zheng Lian, Kuofei Fang, Xuehao Wang, Xinghai Gao, Junqing Wu, Chuyu Wu, Liyi Liu, Yanhan Huang, Keyi Xie, Haomin Ouyang, Jinyang Wu, Fan Zhang, Runhao Zeng, Xun Yang, Bin He · 21. September 2026

  Social Proactive Intelligence (SPI) extends proactive assistance beyond task completeness to consider social appropriateness in diverse embodied scenarios. However, prior SPI research faces two key limitations. First, existing work focuses on static images, whereas dynamic videos provide crucial cue…

• When Should a Failing Robot Ask? Initiating Corrective Human-Robot Dialogue from Audited Sensor Evidence

  Eshika Pathak, Leela Krishna · 21. September 2026

  A robot that fails at a task faces the first decision in corrective dialogue: act on its own diagnosis, consult another onboard sensor, or interrupt a person. Choosing well requires knowing how much the robot's sensors reveal about the cause and how reliable the robot's own diagnosis is. We build a …

• CoreSense: Traceable Failure Recall and Conflict-Aware Belief Gating for Auditable Robot Decisions

  Zoe Li · 18. September 2026

  Robots can recall prior failures without knowing whether recalled evidence remains valid, conflicts with current observations, or is sufficient to guide a decision. We present CoreSense, a robot-system integration architecture that combines traceable episodic evidence with a conflict-aware belief ga…

• HINT-Plan: Human Intention-Aware Robot Task Planning in Context-Rich Environments using Vision Language Models

  Yuchen Liu, Luigi Palmieri, Lujun Li, Radu State, Ilche Georgievski, Marco Aiello · 17. September 2026

  Approaches to incorporating human awareness into mobile robot decision-making mainly focus on collision avoidance in low-level motion planning, often overlooking the challenges posed by human presence and high-level behavior. To address this vacancy, we present HINT-Plan, a novel approach to integra…

• Spook the Machine: Gamified Exploration of Human Imagination of Machine Fear

  Levin Brinkmann, Hiromu Yakura, Sonia Nicoletti, Mar Canet Sola, Thomas F. Eisenmann, Ali Dasmeh, Omar Sherif, Bramantyo Ibrahim Supriyatno, Prateek Gupta, Ignacio Serna, Rodrigo Bermudez Schettino, Iyad Rahwan · 16. September 2026

  What happens when AI machines express fear? Do humans engage differently depending on how they express it? And what does it take to design for affective human-AI interaction? We present Spook the Machine, a gamified platform where participants generate images to frighten AI agents endowed with perso…

• Skill Composition for Legged Robot Reinforcement Learning

  Daniel Gigliotti, Flavio Maiorana, Fabio Patrizi, Luca Iocchi · 15. September 2026

  Robots, and humanoid robots in particular, are increasingly competent at individual behaviors, each obtained by training a specialized controller. A specialized skill is quick to train, converges reliably because the problem it faces is narrow, and can be validated on its own, none of which is true …

• Rethinking the Implications of Human Feedback for Preference Learning in Human-Robot Collaboration

  Qiping Zhang, Kate Candon, Debasmita Ghose, Marynel V\'azquez · 15. September 2026

  In Human-Robot Interaction, the standard approach to learn a reward model that represents human preferences for robot behavior consists of three steps. First, the robot collects limited direct evidence from human feedback (e.g., positive or negative binary feedback). Then, the robot utilizes the dir…

• A Robot Among People:From Social Imitation to the Social Becoming of Human Groups

  Victor Tuan Vu Pham, Judith D\"orrenb\"acher, Thomas H. Weisswange, Marc Hassenzahl · 14. September 2026

  Robots designed to mediate human groups often fall into the solutionist trap: they are framed as sociable agents that fix problems such as conflict, disengagement, or lack of coordination. We suggest a different way of thinking. Rather than discrete agents, robots can be understood as situated eleme…

• CT-SAFR: Safe and Interpretable Chain-of-Thought Reasoning for Autonomous Robots: A Multi-Layered Verification Framework for Trustworthy AI-Driven Robotic Decision Making

  Cagri Temel · 10. September 2026

  Chain-of-Thought (CoT) prompting enables LLMs to perform explicit, step-by-step reasoning, creating opportunities for sophisticated autonomous robots. However, recent research reveals that reasoning models verbalize their actual decision processes only 25-39% of the time, with faithfulness degrading…

• Measuring Language Transfer in Robot Policies: Adding Greek to a Cosmos3 Vision-Language-Action Policy

  Ayoub Kirouane, Georgios Giaples, Christos Petrocheilos · 9. September 2026

  Robot foundation models are trained and evaluated predominantly in English, and robot demonstration corpora do not exist for most languages. We study the addition of Greek to an open vision-language-action stack using only machine-rephrased instructions and no architecture changes. The main challeng…

• SocioGesture: Real-Time and Adaptive Social Gesture Perception for Human-Robot Interaction

  Wenjin Fu, Li-Fan Wu, Jerin Peter, Chip Huyen, Boyuan Chen, Jan Liphardt · 7. September 2026

  Robots interacting with people must recognize not only explicit commands, but also social cues such as invitations, refusals, and unavailability. In real deployments, these cues must be inferred from noisy onboard perception under partial occlusion, changing viewpoints, and strict latency constraint…

• IRWOZ 2.0: A Large Language Model-driven Dialogue Dataset for Industrial Robot Conversations

  Chen Li, Dimitrios Chrysostomou · 4. September 2026

  IRWOZ has improved industrial human-robot interaction (HRI) dialogue systems through domain-specific annotations. However, its initial version contains substantial noise in dialogue states and utterances, limiting state-tracking accuracy. We introduce IRWOZ 2.0, which addresses these limitations thr…

• Towards a Foundational Ontology for Identifying and Resolving Contradictions in Dialogue-based Human-Robot Interactions

  Maitreyee Tewari, Michele Persiani · 3. September 2026

  Existing Human-Robot Interaction (HRI) literature has focused on identifying and structuring errors, failures, conflicts, and knowledge issues (called in this work as contradictions) in domain-specific dialogue-based interactions. However, there is still lack of a formal computational framework to r…

• SocialReasonBench: A Video-QA Benchmark for Social Reasoning with Counterfactual Narrative Videos

  Zheyu Huang, Zijing Shi, Haozhe Luo, Huadong Tang, Mingyu Liu, Meng Fang, Ling Chen · 1. September 2026

  Recent advances in Large Multimodal Models (LMMs) have greatly improved video understanding, yet their ability to reason about human-centered social situations remains limited. Existing benchmarks typically rely on videos with a single observed trajectory, making it difficult to determine whether mo…