Incremental Residual Reinforcement Learning Toward Real-World Learning for Social Navigation
This work addresses the problem of real-world learning for social navigation in robotics, offering a more efficient method for adaptation on edge devices, though it is incremental in nature.
The paper tackled the challenge of enabling mobile robots to adapt to diverse real-world social navigation scenarios by proposing incremental residual RL (IRRL), which integrates incremental learning and residual RL to improve efficiency without a replay buffer. In simulation, IRRL achieved performance comparable to replay buffer-based methods and outperformed existing incremental approaches, with real-world experiments confirming its effectiveness in adapting to unseen environments.
As the demand for mobile robots continues to increase, social navigation has emerged as a critical task, driving active research into deep reinforcement learning (RL) approaches. However, because pedestrian dynamics and social conventions vary widely across different regions, simulations cannot easily encompass all possible real-world scenarios. Real-world RL, in which agents learn while operating directly in physical environments, presents a promising solution to this issue. Nevertheless, this approach faces significant challenges, particularly regarding constrained computational resources on edge devices and learning efficiency. In this study, we propose incremental residual RL (IRRL). This method integrates incremental learning, which is a lightweight process that operates without a replay buffer or batch updates, with residual RL, which enhances learning efficiency by training only on the residuals relative to a base policy. Through the simulation experiments, we demonstrated that, despite lacking a replay buffer, IRRL achieved performance comparable to those of conventional replay buffer-based methods and outperformed existing incremental learning approaches. Furthermore, the real-world experiments confirmed that IRRL can enable robots to effectively adapt to previously unseen environments through the real-world learning.