The Dilemma of Decision-Making in the Real World: When Robots Struggle to Make Choices Due to Situational Constraints

2024-12-02

Towards Autonomous Robotic Systems

AI Summary

Assistive robots frequently encounter “decision deadlock”—a state of prolonged indecision—when operating in dynamic, uncertain environments characterized by sensor occlusion, communication latency, and resource constraints; this issue is exacerbated when serving users with cognitive, motor, or perceptual impairments under noisy, cluttered, or low-illumination conditions. Method: We formally define decision deadlock and propose a context-aware, resilient decision-making framework integrating Bayesian inference, hierarchical reinforcement learning, and symbolic constraint solving. The framework features an uncertainty-aware, multi-granularity fallback mechanism and a context-weighted adaptive arbitration strategy, implemented as a lightweight, online reconfigurable decision module in ROS2. Contribution/Results: Evaluated across six complex indoor navigation tasks, our approach reduces deadlock incidence by 73%, achieves a mean recovery time of only 0.8 seconds, and attains 91.4% generalization accuracy on previously unseen constraint scenarios.

Technology Category

• Intelligent Robots: State Estimation
• Reasoning under Uncertainty: Sequential Decision Making
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Problem

• Uncertain Environments
• Assistance for Health-Impaired Individuals
• Environmental Factors Impact

Innovation

• Decision Scenarios Analysis
• Human-Robot Collaboration
• Uncertain Environments