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Diffusion Model Predictive Control
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We propose Diffusion Model Predictive Control (D-MPC), a novel MPC approach that learns a multi-step action proposal and a multi-step dynamics model, both using diffusion models, and combines them for use in online MPC. On the popular D4RL benchmark, we show performance that is significantly better than existing model-based offline planning methods using MPC (e.g. MBOP) and competitive with state-of-the-art (SOTA) model-based and model-free reinforcement learning methods. We additionally illustrate D-MPC's ability to optimize novel reward functions at run time and adapt to novel dynamics, and highlight its advantages compared to existing diffusion-based planning baselines.
Forward citations
Cited by 6 Pith papers
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On the Guidance of Flow Matching
A unified derivation of energy guidance for general flow matching yields an asymptotically exact Monte Carlo method, approximate gradient methods, and training losses that recover DPS, LGD, and PiGDM as special cases.
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Diffusion-Residual Model Predictive Steering Control for Vehicle Stabilization at the Limit of Handling under Model Uncertainty
Command-conditioned diffusion residual moments resize the MPC yaw reference and chance-tighten the handling envelope, cutting peak side-slip and recovering low-μ stability in simulation at 100 Hz.
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Latent Policy Barrier: Learning Robust Visuomotor Policies by Staying In-Distribution
Latent Policy Barrier improves behavior-cloned visuomotor policies by using a latent dynamics model trained on expert and rollout data to guide actions back toward in-distribution expert states.
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TD-M(PC)$^2$: Improving Temporal Difference MPC Through Policy Constraint
TD-M(PC)2 adds a TD3-BC-style policy constraint to TD-MPC2's policy update, reducing out-of-distribution value queries caused by planner-data mismatch and improving performance on high-dimensional control tasks.
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D-SafeMPC: Diffusion-Driven Safe Model Predictive Control with Discrete-Time Control Barrier Functions
CBF/CLF-guided reverse diffusion plus per-step MPC projection yields higher safety and success rates than prior diffusion-MPC planners on Franka static/dynamic obstacle tasks.
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Generative AI for Autonomous Driving: A Review
A review of generative models (VAEs, GANs, diffusion, transformers, LLMs) applied to map generation, scenario generation, trajectory prediction, and motion planning for autonomous driving.
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