COLSON: Controllable Learning-Based Social Navigation via Diffusion-Based Reinforcement Learning
This work addresses social navigation for autonomous service robots, offering incremental improvements in flexibility and adaptability over existing deep reinforcement learning methods.
The authors tackled the problem of mobile robot navigation in dynamic pedestrian environments by applying a diffusion-based reinforcement learning approach, which outperformed traditional methods and enabled post-training action smoothing and adaptation to static obstacles.
Mobile robot navigation in dynamic environments with pedestrian traffic is a key challenge in the development of autonomous mobile service robots. Recently, deep reinforcement learning-based methods have been actively studied and have outperformed traditional rule-based approaches owing to their optimization capabilities. Among these, methods that assume a continuous action space typically rely on a Gaussian distribution assumption, which limits the flexibility of generated actions. Meanwhile, the application of diffusion models to reinforcement learning has advanced, allowing for more flexible action distributions compared with Gaussian distribution-based approaches. In this study, we applied a diffusion-based reinforcement learning approach to social navigation and validated its effectiveness. Furthermore, by leveraging the characteristics of diffusion models, we propose an extension that enables post-training action smoothing and adaptation to static obstacle scenarios not considered during the training steps.