Explicit World Models for Reliable Human-Robot Collaboration

• URL: http://arxiv.org/abs/2601.01705v2
• Date: Mon, 12 Jan 2026 01:05:05 GMT
• Title: Explicit World Models for Reliable Human-Robot Collaboration
• Authors: Kenneth Kwok, Basura Fernando, Qianli Xu, Vigneshwaran Subbaraju, Dongkyu Choi, Boon Kiat Quek,
• Abstract summary: We take a radically different tack to the issue of reliable embodied AI.<n>We emphasise the dynamic, ambiguous and subjective nature of human-robot interactions.
• Score: 13.90528067304433
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper addresses the topic of robustness under sensing noise, ambiguous instructions, and human-robot interaction. We take a radically different tack to the issue of reliable embodied AI: instead of focusing on formal verification methods aimed at achieving model predictability and robustness, we emphasise the dynamic, ambiguous and subjective nature of human-robot interactions that requires embodied AI systems to perceive, interpret, and respond to human intentions in a manner that is consistent, comprehensible and aligned with human expectations. We argue that when embodied agents operate in human environments that are inherently social, multimodal, and fluid, reliability is contextually determined and only has meaning in relation to the goals and expectations of humans involved in the interaction. This calls for a fundamentally different approach to achieving reliable embodied AI that is centred on building and updating an accessible "explicit world model" representing the common ground between human and AI, that is used to align robot behaviours with human expectations.

Related papers

• Towards Immersive Human-X Interaction: A Real-Time Framework for Physically Plausible Motion Synthesis [51.95817740348585]
  Human-X is a novel framework designed to enable immersive and physically plausible human interactions across diverse entities.<n>Our method jointly predicts actions and reactions in real-time using an auto-regressive reaction diffusion planner.<n>Our framework is validated in real-world applications, including virtual reality interface for human-robot interaction.
  arXiv  Detail & Related papers  (2025-08-04T06:35:48Z)
• The Human Robot Social Interaction (HSRI) Dataset: Benchmarking Foundational Models' Social Reasoning [49.32390524168273]
  Our work aims to advance the social reasoning of embodied artificial intelligence (AI) agents in real-world social interactions.<n>We introduce a large-scale real-world Human Robot Social Interaction (HSRI) dataset to benchmark the capabilities of language models (LMs) and foundational models (FMs)<n>Our dataset consists of 400 real-world human social robot interaction videos and over 10K annotations, detailing the robot's social errors, competencies, rationale, and corrective actions.
  arXiv  Detail & Related papers  (2025-04-07T06:27:02Z)
• Does Your AI Agent Get You? A Personalizable Framework for Approximating Human Models from Argumentation-based Dialogue Traces [4.178382980763478]
  We present a framework that enables AI agents to adapt their understanding of human users through argumentation-based dialogues.<n>Our approach, called Persona, draws on prospect theory and integrates a probability weighting function with a Bayesian belief update mechanism.<n>We demonstrate that Persona effectively captures evolving human beliefs, facilitates personalized interactions, and outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-02-22T22:37:16Z)
• Humans Coexist, So Must Embodied Artificial Agents [13.570292971478665]
  coexistence is a prerequisite for long-term, in-the-wild interaction with humans.<n>We propose key research directions for the artificial intelligence community to develop coexisting embodied agents.
  arXiv  Detail & Related papers  (2025-02-07T10:28:39Z)
• Position: Towards Bidirectional Human-AI Alignment [109.57781720848669]
  We argue that the research community should explicitly define and critically reflect on "alignment" to account for the bidirectional and dynamic relationship between humans and AI.<n>We introduce the Bidirectional Human-AI Alignment framework, which not only incorporates traditional efforts to align AI with human values but also introduces the critical, underexplored dimension of aligning humans with AI.
  arXiv  Detail & Related papers  (2024-06-13T16:03:25Z)
• Approximating Human Models During Argumentation-based Dialogues [4.178382980763478]
  Key challenge in Explainable AI Planning (XAIP) is model reconciliation. We propose a novel framework that enables AI agents to learn and update a probabilistic human model.
  arXiv  Detail & Related papers  (2024-05-28T23:22:18Z)
• Towards interactive evaluations for interaction harms in human-AI systems [8.989911701384788]
  We propose a shift towards evaluation based on textitinteractional ethics, which focuses on textitinteraction harms<n>First, we discuss the limitations of current evaluation methods, which (1) are static, (2) assume a universal user experience, and (3) have limited construct validity.<n>We present practical principles for designing interactive evaluations. These include ecologically valid interaction scenarios, human impact metrics, and diverse human participation approaches.
  arXiv  Detail & Related papers  (2024-05-17T08:49:34Z)
• Real-time Addressee Estimation: Deployment of a Deep-Learning Model on the iCub Robot [52.277579221741746]
  Addressee Estimation is a skill essential for social robots to interact smoothly with humans. Inspired by human perceptual skills, a deep-learning model for Addressee Estimation is designed, trained, and deployed on an iCub robot. The study presents the procedure of such implementation and the performance of the model deployed in real-time human-robot interaction.
  arXiv  Detail & Related papers  (2023-11-09T13:01:21Z)
• Robust Planning for Human-Robot Joint Tasks with Explicit Reasoning on Human Mental State [2.8246074016493457]
  We consider the human-aware task planning problem where a human-robot team is given a shared task with a known objective to achieve. Recent approaches tackle it by modeling it as a team of independent, rational agents, where the robot plans for both agents' (shared) tasks. We describe a novel approach to solve such problems, which models and uses execution-time observability conventions.
  arXiv  Detail & Related papers  (2022-10-17T09:21:00Z)
• Data-driven emotional body language generation for social robotics [58.88028813371423]
  In social robotics, endowing humanoid robots with the ability to generate bodily expressions of affect can improve human-robot interaction and collaboration. We implement a deep learning data-driven framework that learns from a few hand-designed robotic bodily expressions. The evaluation study found that the anthropomorphism and animacy of the generated expressions are not perceived differently from the hand-designed ones.
  arXiv  Detail & Related papers  (2022-05-02T09:21:39Z)
• A Cognitive Framework for Delegation Between Error-Prone AI and Human Agents [0.0]
  We investigate the use of cognitively inspired models of behavior to predict the behavior of both human and AI agents. The predicted behavior is used to delegate control between humans and AI agents through the use of an intermediary entity.
  arXiv  Detail & Related papers  (2022-04-06T15:15:21Z)
• Joint Mind Modeling for Explanation Generation in Complex Human-Robot Collaborative Tasks [83.37025218216888]
  We propose a novel explainable AI (XAI) framework for achieving human-like communication in human-robot collaborations. The robot builds a hierarchical mind model of the human user and generates explanations of its own mind as a form of communications. Results show that the generated explanations of our approach significantly improves the collaboration performance and user perception of the robot.
  arXiv  Detail & Related papers  (2020-07-24T23:35:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.