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

Muhammad Adel Yusuf, Ali Nasir, Zeeshan Hameed Khan

TL;DR

This work tackles safe and efficient human–robot collaboration in industrial settings by modeling bilateral intent under uncertainty. It advances a POMDP-based framework that jointly reasons about safety (via a tri-circle concept), human emotional states (motivation and aggression), and task priorities to guide cobot decisions. The approach extends MDP principles with belief-state handling and a practical action-selection heuristic to operate online, demonstrated in a numerical case study with sequential tasks and velocity-based observations. The results indicate improved safety, adaptability, and collaboration efficiency, with potential impact on Industry 4.0 cobot deployments where human factors are critical for performance.

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.

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

TL;DR

This work tackles safe and efficient human–robot collaboration in industrial settings by modeling bilateral intent under uncertainty. It advances a POMDP-based framework that jointly reasons about safety (via a tri-circle concept), human emotional states (motivation and aggression), and task priorities to guide cobot decisions. The approach extends MDP principles with belief-state handling and a practical action-selection heuristic to operate online, demonstrated in a numerical case study with sequential tasks and velocity-based observations. The results indicate improved safety, adaptability, and collaboration efficiency, with potential impact on Industry 4.0 cobot deployments where human factors are critical for performance.

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.
Paper Structure (18 sections, 27 equations, 13 figures, 6 tables)

This paper contains 18 sections, 27 equations, 13 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Background and Related Work
  3. Key Aspects of the Proposed Approach
  4. Safety Circles
  5. Human Emotional States and Motivation Level
  6. Task Assignment and Priorities
  7. Human-Robot Collaboration Model
  8. State Space
  9. Decision Variables
  10. Observation Variables
  11. State Transition Mapping
  12. Formulation of the Cost Function
  13. Decision-Making under Uncertainty and MDP
  14. Closed loop implementation (Online trajectory)
  15. Numerical Case study
  16. ...and 3 more sections

Figures (13)

  • Figure 1: Bilateral intent prediction and recognition in Collaboration.
  • Figure 2: Multi-modal sensing and classification of the human mood and well-being.
  • Figure 3: Three circles of safety.
  • Figure 4: An equivalence between Maslow's theory of motivation for humans vs. Robot’s motivation in Collaboration.
  • Figure 5: State Variable Mapping.
  • ...and 8 more figures