Goal-Oriented Dynamic Weight Optimization for Multi-Object Navigation

Area of Science:

• Robotics
• Artificial Intelligence
• Computer Vision

Background:

• Multi-object navigation (MON) requires sequential target localization in unknown environments, demanding long-term planning under incomplete information.
• Current methods often prioritize local path optimization, leading to inefficiencies like slower convergence in sparse reward settings and increased risk of deadlocks.
• A key challenge in MON is the deformation of the shared decision space due to independent subtask optimization, resulting in redundant paths.

Purpose of the Study:

• To address the limitations of current MON methods by proposing a novel algorithm for dynamic, cross-task optimization.
• To develop a method that adaptively balances task contributions and minimizes overall effort through weight adjustment.
• To improve convergence efficiency and navigation performance in sparse reward settings.

Main Methods:

• Introduction of the Goal-oriented Dynamic Weight Optimization (GDWO) algorithm, integrating target-specific value loss functions into a unified framework.
• Dynamic weight adjustment using gradient-based updates, with normalization based on navigation success rates to prioritize challenging targets and prevent over-optimization.
• Incorporation of auxiliary modules: prior knowledge-based navigation and frontier-aware exploration to enhance performance.

Main Results:

• GDWO demonstrated significant improvements in key metrics for multi-object navigation tasks on Gibson and Matterport3D datasets.
• The algorithm effectively optimizes path planning, leading to reduced exploration costs and enhanced navigation efficiency.
• GDWO successfully unifies multiple objectives within a single decision space, balancing short-term gains with long-term goals.

Conclusions:

• GDWO offers an effective solution for multi-object navigation by enabling adaptive, goal-oriented optimization.
• The proposed method enhances agent performance in complex environments by improving planning, reducing costs, and increasing efficiency.
• GDWO represents a significant advancement in addressing the challenges of sparse rewards and dynamic decision-making in MON tasks.