mlsanityData to 7 Oct 2026

Adaptive Linear Path Model-Based Diffusion

arXiv 2602.02831 ↗First posted 2 Feb 2026cs.ROcited by 0

On the map: Diffusion models → Diffusion and flow policies for robotics

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Diffusion and flow policies for robotics, one of 14 directions in Diffusion models. Builds on 25 of its 38 references that are in the map. First posted on arXiv 2 Feb 2026.

Builds on

25 of 38 references.

Closest ancestors · 6

Same direction, newest first

  1. 2025Steering Your Diffusion Policy with Latent Space Reinforcement Learning18 Jun
  2. 2025Generative Predictive Control: Flow Matching Policies for Dynamic and Difficult-to-Demonstrate Tasks19 Feb
  3. 2024Model-Based Diffusion for Trajectory Optimization28 May
  4. 2024Feedback Efficient Online Fine-Tuning of Diffusion Models26 Feb
  5. 2023Directly Fine-Tuning Diffusion Models on Differentiable Rewards29 Sep
  6. 2023Diffusion Policy: Visuomotor Policy Learning via Action Diffusion7 Mar

Other references · 19

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2017Proximal Policy Optimization Algorithmscited by 14,441
  3. 2020Denoising Diffusion Implicit Modelscited by 9,655
  4. 2022Flow Matching for Generative Modelingcited by 5,426
  5. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
  6. 2023SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesiscited by 4,290
  7. 2015Continuous control with deep reinforcement learningcited by 3,279
  8. 2018Soft Actor-Critic Algorithms and Applicationscited by 1,179
  9. 2022Planning with Diffusion for Flexible Behavior Synthesiscited by 1,023
  10. 2020CURL: Contrastive Unsupervised Representations for Reinforcement Learningcited by 745
  11. 2023TD-MPC2: Scalable, Robust World Models for Continuous Controlcited by 482
  12. 2021Brax -- A Differentiable Physics Engine for Large Scale Rigid Body Simulationcited by 375
7 more
  1. 2022Temporal Difference Learning for Model Predictive Controlcited by 375
  2. 2024Flow Matching Guide and Codecited by 291
  3. 2018Learning to Walk via Deep Reinforcement Learningcited by 153
  4. 2022An optimal control perspective on diffusion-based generative modelingcited by 145
  5. 2024Full-Order Sampling-Based MPC for Torque-Level Locomotion Control via Diffusion-Style Annealingcited by 20
  6. 2020Neural-Swarm2: Planning and Control of Heterogeneous Multirotor Swarms using Learned Interactionscited by 19
  7. 2024Bisimulation metric for Model Predictive Controlcited by 6

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Close in wording, no citation either way

  1. 2026Model-Based Diffusion Optimal Control for Multi-Robot Motion Planning
  2. 2026Motion Planning with Model-Based Diffusion via Constraint Optimization and Adaptive Scheduling
  3. 2024Diffusion Policy Policy Optimization
  4. 2025Model-Based Diffusion Sampling for Predictive Control in Offline Decision Making
  5. 2026From Noise to Control: Parameterized Diffusion Policies
  6. 2026Beyond Imitation: Reinforcement Learning Fine-Tuning for Adaptive Diffusion Navigation Policies
  7. 2024Motion Planning Diffusion: Learning and Adapting Robot Motion Planning with Diffusion Models
  8. 2025Adaptive Diffusion Policy Optimization for Robotic Manipulation