SANGO: Socially Aware Navigation through Grouped Obstacles
On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control
Abstract (arXiv)
This paper introduces SANGO (Socially Aware Navigation through Grouped Obstacles), a novel method that ensures socially appropriate behavior by dynamically grouping obstacles and adhering to social norms. Using deep reinforcement learning, SANGO trains agents to navigate complex environments leveraging the DBSCAN algorithm for obstacle clustering and Proximal Policy Optimization (PPO) for path planning. The proposed approach improves safety and social compliance by maintaining appropriate distances and reducing collision rates. Extensive experiments conducted in custom simulation environments demonstrate SANGO's superior performance in significantly reducing discomfort (by up to 83.5%), reducing collision rates (by up to 29.4%) and achieving higher successful navigation in dynamic and crowded scenarios. These findings highlight the potential of SANGO for real-world applications, paving the way for advanced socially adept robotic navigation systems.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods. Builds on 4 of its 18 references that are in the map. First posted on arXiv 29 Nov 2024.
Builds on
4 of 18 references.
References · 4
- 2017Proximal Policy Optimization Algorithmscited by 15,215
- 2016OpenAI Gymcited by 1,736
- 2021Core Challenges of Social Robot Navigation: A Surveycited by 106
- 2021Group-based Motion Prediction for Navigation in Crowded Environmentscited by 15
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- 2025Socially aware navigation for mobile robots: a survey on deep reinforcement learning approaches
- 2024Socially Integrated Navigation: A Social Acting Robot with Deep Reinforcement Learning
- 2022Socially Compliant Navigation Dataset (SCAND): A Large-Scale Dataset of Demonstrations for Social Navigation
- 2024Adaptive Social Force Window Planner with Reinforcement Learning
- 2026Towards Socially Compliant Navigation in Deep Reinforcement Learning via Proxemics-Based Reward Modeling
- 2020NaviGAN: A Generative Approach for Socially Compliant Navigation
- 2026SoGuDiff: Socially Guided Diffusion for Steerable, Norm-Grounded Robot Navigation
- 2025A Comparative Study of Human Motion Models in Reinforcement Learning Algorithms for Social Robot Navigation