用扩散模型提升机器人社交导航的灵活性和泛化能力
COLSON: Controllable Learning-Based Social Navigation via Diffusion-Based Reinforcement Learning
- 基于扩散模型的强化学习方法生成更灵活的动作分布
- 在未见过的障碍物和目标任务下无需再训练即可适应
- 适合需要动态避障与多任务切换的服务机器人场景
动态人流环境中的移动机器人导航是自主服务机器人发展的关键挑战。近年来，基于深度强化学习的方法因优化能力强，已超越传统规则方法。但多数采用连续动作空间的模型依赖高斯分布，限制了动作灵活性。扩散模型在强化学习中的应用提升了动作分布的表达能力。本文将扩散模型引入社交导航，验证其有效性，并利用扩散模型特性，提出无需额外训练即可适应未见场景的扩展方法。具体包括：训练中未出现的静态障碍物环境，以及训练时目标不同（如伴随目标行人避开他人到达目的地）的情形。
原文摘要 · Abstract (English)
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, those that assume continuous action spaces typically rely on Gaussian distributions, which limit the flexibility of the generated actions. In contrast, the application of diffusion models to reinforcement learning has advanced, enabling more flexible action distributions than Gaussian policy-based approaches. In this study, we apply a diffusion-based reinforcement learning approach to social navigation and validate its effectiveness. Furthermore, by exploiting the characteristics of diffusion models, we propose extensions that enable adaptation to previously unseen scenarios without additional training. As concrete scenario examples, we demonstrate adaptability to scenarios in which static obstacles exist in the environment that were not present during training, as well as scenarios in which the objective differs from training, such as accompanying target pedestrians while avoiding others to reach the destination.
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