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. Real-world videos and code are available at https://gmpc-imle.github.io/.

翻译：扩散模型近期在轨迹规划领域展现出强劲性能，因其能够捕捉复杂行为的多样化多模态分布。这类模型的关键局限在于推理速度缓慢——这源于其迭代去噪过程，使其难以适应闭环模型预测控制（MPC）等实时应用场景，其中需快速生成规划并持续适应动态环境。本文探索了隐式最大似然估计（IMLE）作为替代生成式规划方法的可行性：IMLE在保证强模态覆盖的同时，可实现两个数量级的推理加速，特别适用于实时MPC任务。实验结果表明，相较于标准扩散规划器，IMLE在标准离线强化学习基准测试中达到竞争性性能，同时在开环与闭环场景中均显著提升规划速度。我们进一步在实时闭环人类导航场景中验证了IMLE，展示其在动态环境中实现快速自适应规划生成的能力。真实世界视频与代码已开源至 https://gmpc-imle.github.io/。