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

Adaptive Social Force Window Planner with Reinforcement Learning / Martini, Mauro; Pérez-Higueras, Noé; Ostuni, Andrea; Chiaberge, Marcello; Caballero, Fernando; Merino, Luis. - (2024), pp. 4816-4822. (Intervento presentato al convegno 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) tenutosi a Abu Dhabi (UAE) nel 14-18 October 2024) [10.1109/iros58592.2024.10802383].

Adaptive Social Force Window Planner with Reinforcement Learning

Martini, Mauro;Ostuni, Andrea;Chiaberge, Marcello;
2024

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
2024
979-8-3503-7770-5
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2996179