Mobile Robots through Task-Based Human Instructions using Incremental Curriculum Learning
On the map: Deep reinforcement learning methods → RL for robot locomotion and manipulation
Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods. Builds on 19 of its 27 references that are in the map. First posted on arXiv 26 Dec 2024.
Builds on
19 of 27 references.
References · 19
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7 more
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- 2024Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes
- 2025Taxonomy and Trends in Reinforcement Learning for Robotics and Control Systems: A Structured Review
- 2024CurricuLLM: Automatic Task Curricula Design for Learning Complex Robot Skills using Large Language Models
- 2026Incremental Residual Reinforcement Learning Toward Real-World Learning for Social Navigation
- 2026Decoupling Task and Behavior: A Two-Stage Reward Curriculum in Reinforcement Learning for Robotics
- 2024Advancing Household Robotics: Deep Interactive Reinforcement Learning for Efficient Training and Enhanced Performance
- 2024Grounded Curriculum Learning
- 2025Socially aware navigation for mobile robots: a survey on deep reinforcement learning approaches