Adaptive Social Force Window Planner with Reinforcement Learning

TL;DR

This paper introduces an adaptive social navigation planner for mobile robots that combines classic path planning with reinforcement learning to dynamically tune social costs, improving performance in socially aware navigation tasks.

Contribution

It presents a novel Deep Reinforcement Learning-based method to adaptively adjust social cost parameters in a Dynamic Window Approach, enhancing social navigation capabilities.

Findings

Outperforms static cost planners in various environments

Demonstrates improved social compliance and efficiency

Shows robustness across different social scenarios

Abstract

Human-aware navigation is a complex task for mobile robots, requiring an autonomous navigation system capable of achieving efficient path planning together with socially compliant behaviors. Social planners usually add costs or constraints to the objective function, leading to intricate tuning processes or tailoring the solution to the specific social scenario. Machine Learning can enhance planners' versatility and help them learn complex social behaviors from data. This work proposes an adaptive social planner, using a Deep Reinforcement Learning agent to dynamically adjust the weighting parameters of the cost function used to evaluate trajectories. The resulting planner combines the robustness of the classic Dynamic Window Approach, integrated with a social cost based on the Social Force Model, and the flexibility of learning methods to boost the overall performance on social navigation tasks. Our extensive experimentation on different environments demonstrates the general advantage of the proposed method over static cost planners.