Incremental Residual Reinforcement Learning Toward Real-World Learning for Social Navigation

Researchers propose Incremental Residual Reinforcement Learning (IRRL), a new method that enables mobile robots to learn social navigation directly in physical environments without requiring large computational resources or replay buffers. The approach combines incremental learning with residual reinforcement learning to improve efficiency, achieving performance comparable to traditional methods while enabling real-world adaptation.

This research addresses a fundamental challenge in robotics: deploying learning systems on resource-constrained edge devices that must operate in unpredictable real-world environments. Social navigation—the ability to move through spaces with pedestrians while following social conventions—varies dramatically by region and culture, making simulation-only training insufficient. IRRL tackles this by eliminating the replay buffer requirement, a standard but computationally expensive component of most deep reinforcement learning systems.

The innovation combines two complementary approaches: incremental learning operates sequentially without storing historical data, reducing memory demands on edge devices, while residual learning focuses computational effort on learning only the differences from a base policy rather than entire behaviors from scratch. This architectural choice matters because typical mobile robots have limited onboard processing power, making conventional RL methods impractical for continuous deployment.

The real-world validation is particularly significant—simulation experiments alone rarely translate to physical deployments. The researchers demonstrated that IRRL can enable robots to generalize to previously unseen environments through live learning, suggesting the method handles the distribution gap between simulated and real pedestrian dynamics. This capability directly impacts the commercial viability of autonomous robot navigation systems in dynamic urban environments.

The implications extend beyond robotics research. As autonomous systems proliferate in logistics, delivery, and service industries, the ability to deploy learning algorithms on edge hardware while maintaining safety and efficiency becomes economically critical. Success here could accelerate the timeline for autonomous mobile robot adoption in pedestrian-heavy environments where current systems underperform.

• →IRRL eliminates replay buffer requirements, enabling efficient learning on computationally constrained robot hardware
• →Real-world learning validation demonstrates robots can adapt to unseen environments without pure simulation training
• →The method combines incremental and residual learning to improve efficiency while maintaining performance parity with standard approaches
• →Regional variations in pedestrian dynamics and social conventions require robots to learn in actual deployment environments
• →Success could accelerate autonomous mobile robot commercialization in dynamic urban and logistics applications