Stochastic Decision-Making Framework for Human-Robot Collaboration in Industrial Applications

• URL: http://arxiv.org/abs/2601.14809v1
• Date: Wed, 21 Jan 2026 09:35:33 GMT
• Title: Stochastic Decision-Making Framework for Human-Robot Collaboration in Industrial Applications
• Abstract summary: Collaborative robots, or cobots, are increasingly integrated into various industrial and service settings to work efficiently and safely alongside humans.<n>This paper proposes an approach for decision-making in human-robot collaborative environments utilizing modeling.
• Score: 1.9116784879310027
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Collaborative robots, or cobots, are increasingly integrated into various industrial and service settings to work efficiently and safely alongside humans. However, for effective human-robot collaboration, robots must reason based on human factors such as motivation level and aggression level. This paper proposes an approach for decision-making in human-robot collaborative (HRC) environments utilizing stochastic modeling. By leveraging probabilistic models and control strategies, the proposed method aims to anticipate human actions and emotions, enabling cobots to adapt their behavior accordingly. So far, most of the research has been done to detect the intentions of human co-workers. This paper discusses the theoretical framework, implementation strategies, simulation results, and potential applications of the bilateral collaboration approach for safety and efficiency in collaborative robotics.

Related papers

• CollabSkill: Evaluating Human-Agent Collaboration On Real-World Tasks [55.84031315625504]
  CollabSkill is a framework for evaluating human-agent collaboration on real-world occupational tasks.<n>It pairs real human workers with AI agents on tasks matched to their occupational background.<n>Our analysis reveals that practical experience emerges as the primary driver of collaboration skill.
  arXiv  Detail & Related papers  (2026-04-20T03:03:25Z)
• Mixed-Initiative Dialog for Human-Robot Collaborative Manipulation [8.446410154654467]
  MICoBot handles the common scenario where both agents, using natural language, take initiative in formulating, accepting, or rejecting proposals on who can best complete different steps of a task.<n>To handle diverse, task-directed dialog, MICoBot makes decisions at three levels: (1) a meta-planner considers human dialog to formulate and code a high-level collaboration strategy, (2) a planner optimally allocates the remaining steps to either agent based on the robot's capabilities, and (3) an action decides the low-level actions to perform or words to say to the human.
  arXiv  Detail & Related papers  (2025-08-07T16:09:12Z)
• Habitat 3.0: A Co-Habitat for Humans, Avatars and Robots [119.55240471433302]
  Habitat 3.0 is a simulation platform for studying collaborative human-robot tasks in home environments. It addresses challenges in modeling complex deformable bodies and diversity in appearance and motion. Human-in-the-loop infrastructure enables real human interaction with simulated robots via mouse/keyboard or a VR interface.
  arXiv  Detail & Related papers  (2023-10-19T17:29:17Z)
• CoGrasp: 6-DoF Grasp Generation for Human-Robot Collaboration [0.0]
  We propose a novel, deep neural network-based method called CoGrasp that generates human-aware robot grasps. In real robot experiments, our method achieves about 88% success rate in producing stable grasps. Our approach enables a safe, natural, and socially-aware human-robot objects' co-grasping experience.
  arXiv  Detail & Related papers  (2022-10-06T19:23:25Z)
• The Road to a Successful HRI: AI, Trust and ethicS-TRAITS [64.77385130665128]
  The aim of this workshop is to foster the exchange of insights on past and ongoing research towards effective and long-lasting collaborations between humans and robots. We particularly focus on AI techniques required to implement autonomous and proactive interactions.
  arXiv  Detail & Related papers  (2022-06-07T11:12:45Z)
• Co-GAIL: Learning Diverse Strategies for Human-Robot Collaboration [51.268988527778276]
  We present a method for learning a human-robot collaboration policy from human-human collaboration demonstrations. Our method co-optimizes a human policy and a robot policy in an interactive learning process.
  arXiv  Detail & Related papers  (2021-08-13T03:14:43Z)
• Show Me What You Can Do: Capability Calibration on Reachable Workspace for Human-Robot Collaboration [83.4081612443128]
  We show that a short calibration using REMP can effectively bridge the gap between what a non-expert user thinks a robot can reach and the ground-truth. We show that this calibration procedure not only results in better user perception, but also promotes more efficient human-robot collaborations.
  arXiv  Detail & Related papers  (2021-03-06T09:14:30Z)
• Forming Human-Robot Cooperation for Tasks with General Goal using Evolutionary Value Learning [9.053709318841232]
  In Human-Robot Cooperation (HRC), the robot cooperates with humans to accomplish the task together. Existing approaches assume the human has a specific goal during the cooperation, and the robot infers and acts toward it. We present the Evolutionary Value Learning (EVL) approach to model the dynamics of the goal specification process in HRC.
  arXiv  Detail & Related papers  (2020-12-19T20:27:09Z)
• 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)
• Deployment and Evaluation of a Flexible Human-Robot Collaboration Model Based on AND/OR Graphs in a Manufacturing Environment [2.3848738964230023]
  A major bottleneck to effectively deploy collaborative robots to manufacturing industries is developing task planning algorithms. A pick-and-place palletization task, which requires the collaboration between humans and robots, is investigated. The results of this study demonstrate how human-robot collaboration models like the one we propose can leverage the flexibility and the comfort of operators in the workplace.
  arXiv  Detail & Related papers  (2020-07-13T22:05:34Z)

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.