Implicit Maximum Likelihood Estimation for Real-time Generative Model Predictive Control
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 ...
| Authors | Grayson Lee, Minh Bui, Shuzi Zhou et al. |
|---|---|
| Primary category | cs.RO |
| Published | 14 March 2026 |
| Last updated at source | 22 March 2026 |
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