The interest in combining model-based control approaches with diffusion models has been growing. Although we have seen many impressive robotic control results in difficult tasks, the performance of diffusion models is highly sensitive to the choice of scheduling parameters, making parameter tuning one of the most critical challenges. We introduce Linear Path Model-Based Diffusion (LP-MBD), which replaces the variance-preserving schedule with a flow-matching-inspired linear probability path. This yields a geometrically interpretable and decoupled parameterization that reduces tuning complexity and provides a stable foundation for adaptation. Building on this, we propose Adaptive LP-MBD (ALP-MBD), which leverages reinforcement learning to adjust diffusion steps and noise levels according to task complexity and environmental conditions. Across numerical studies, Brax benchmarks, and mobile-robot trajectory tracking, LP-MBD simplifies scheduling while maintaining strong performance, and ALP-MBD further improves robustness, adaptability, and real-time efficiency.

翻译：将基于模型的控制方法与扩散模型相结合的研究兴趣日益增长。尽管我们已在复杂任务中观察到许多令人印象深刻的机器人控制结果，但扩散模型的性能对调度参数的选择高度敏感，使得参数调优成为最关键的挑战之一。本文提出线性路径模型基扩散（LP-MBD），该方法以流匹配启发的线性概率路径替代方差保持调度，从而产生几何可解释且解耦的参数化方案，降低了调优复杂度，并为适应性调整提供了稳定基础。在此基础上，我们进一步提出适应性线性路径模型基扩散（ALP-MBD），该方法利用强化学习根据任务复杂度与环境条件动态调整扩散步数与噪声水平。在数值研究、Brax基准测试以及移动机器人轨迹跟踪实验中，LP-MBD在保持优异性能的同时简化了调度机制，而ALP-MBD则进一步提升了系统的鲁棒性、适应性与实时效率。