Goal-oriented Dynamic Weight Optimization for Multi-Object Navigation | JoVE Visualize

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.