[2603.05113] Decoupling Task and Behavior: A Two-Stage Reward Curriculum in Reinforcement Learning for Robotics

Computer Science > Machine Learning arXiv:2603.05113 (cs) [Submitted on 5 Mar 2026] Title:Decoupling Task and Behavior: A Two-Stage Reward Curriculum in Reinforcement Learning for Robotics Authors:Kilian Freitag, Knut Åkesson, Morteza Haghir Chehreghani View a PDF of the paper titled Decoupling Task and Behavior: A Two-Stage Reward Curriculum in Reinforcement Learning for Robotics, by Kilian Freitag and 1 other authors View PDF HTML (experimental) Abstract:Deep Reinforcement Learning is a promising tool for robotic control, yet practical application is often hindered by the difficulty of designing effective reward functions. Real-world tasks typically require optimizing multiple objectives simultaneously, necessitating precise tuning of their weights to learn a policy with the desired characteristics. To address this, we propose a two-stage reward curriculum where we decouple task-specific objectives from behavioral terms. In our method, we first train the agent on a simplified task-only reward function to ensure effective exploration before introducing the full reward that includes auxiliary behavior-related terms such as energy efficiency. Further, we analyze various transition strategies and demonstrate that reusing samples between phases is critical for training stability. We validate our approach on the DeepMind Control Suite, ManiSkill3, and a mobile robot environment, modified to include auxiliary behavioral objectives. Our method proves to be simple yet effective, ...