[2603.13733] Implicit Maximum Likelihood Estimation for Real-time Generative Model Predictive Control

Computer Science > Robotics arXiv:2603.13733 (cs) [Submitted on 14 Mar 2026 (v1), last revised 22 Mar 2026 (this version, v2)] Title:Implicit Maximum Likelihood Estimation for Real-time Generative Model Predictive Control Authors:Grayson Lee, Minh Bui, Shuzi Zhou, Yankai Li, Mo Chen, Ke Li View a PDF of the paper titled Implicit Maximum Likelihood Estimation for Real-time Generative Model Predictive Control, by Grayson Lee and 5 other authors View PDF HTML (experimental) Abstract:Diffusion-based models have recently shown strong performance in trajectory planning, as they are capable of capturing diverse, multimodal distributions of complex behaviors. A key limitation of these models is their slow inference speed, which results from the iterative denoising process. This makes them less suitable for real-time applications such as closed-loop model predictive control (MPC), where plans must be generated quickly and adapted continuously to a changing environment. In this paper, we investigate Implicit Maximum Likelihood Estimation (IMLE) as an alternative generative modeling approach for planning. IMLE offers strong mode coverage while enabling inference that is two orders of magnitude faster, making it particularly well suited for real-time MPC tasks. Our results demonstrate that IMLE achieves competitive performance on standard offline reinforcement learning benchmarks compared to the standard diffusion-based planner, while substantially improving planning speed in both ope...