Implicit Maximum Likelihood Estimation for Real-time Generative Model Predictive Control
This addresses the real-time planning bottleneck for applications like closed-loop model predictive control, representing an incremental improvement over existing generative approaches.
The paper tackles the slow inference speed of diffusion-based models for trajectory planning by proposing Implicit Maximum Likelihood Estimation (IMLE), which achieves two orders of magnitude faster inference while maintaining competitive performance on standard benchmarks.
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 open-loop and closed-loop settings. We further validate IMLE in a closed-loop human navigation scenario, operating in real-time, demonstrating how it enables rapid and adaptive plan generation in dynamic environments.