Mobile Robots through Task-Based Human Instructions using Incremental Curriculum Learning
This is an incremental improvement for mobile robotics in dynamic indoor environments.
This paper tackles mobile robot navigation through human instructions by integrating incremental curriculum learning with deep reinforcement learning, demonstrating that robots trained with this framework outperform those without curriculum learning.
This paper explores the integration of incremental curriculum learning (ICL) with deep reinforcement learning (DRL) techniques to facilitate mobile robot navigation through task-based human instruction. By adopting a curriculum that mirrors the progressive complexity encountered in human learning, our approach systematically enhances robots' ability to interpret and execute complex instructions over time. We explore the principles of DRL and its synergy with ICL, demonstrating how this combination not only improves training efficiency but also equips mobile robots with the generalization capability required for navigating through dynamic indoor environments. Empirical results indicate that robots trained with our ICL-enhanced DRL framework outperform those trained without curriculum learning, highlighting the benefits of structured learning progressions in robotic training.