{"ID":6536428,"CreatedAt":"2026-07-14T01:21:01.169441415Z","UpdatedAt":"2026-07-14T12:24:04.010706258Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.10288","arxiv_id":"2607.10288","title":"PIER-Flow: Physics-Informed Efficient Rectified Flow for Real-Time Mobile Robot Navigation","abstract":"Autonomous navigation in dense and highly dynamic environments requires both physically feasible control and low-latency replanning. Optimization-based methods such as Model Predictive Control (MPC) explicitly handle robot kinematics and safety constraints, but repeated nonlinear optimization can limit real-time responsiveness. Deterministic behavior-cloning policies enable efficient inference but may fail to represent multimodal avoidance behaviors, whereas diffusion policies capture multimodality at the cost of time-consuming iterative denoising. We propose PIER-Flow (Physics-Informed Efficient Rectified Flow), a lightweight navigation policy for mobile robots. By distilling an MPC expert into a continuous-time Ordinary Differential Equation (ODE), PIER-Flow achieves single-step action generation through parallel latent sampling and lightweight feasibility selection. We introduce a physics-informed training objective to enforce kinematic consistency, paired with an asynchronous action chunking architecture for robust sim-to-real deployment. Extensive simulations demonstrate that PIER-Flow achieves a 98.85\\% success rate and zero collisions, with an average inference of $\\sim$1.29 ms, which accelerates planning by 37.2$\\times$ compared to MPC and over 800$\\times$ against standard diffusion models. Crucially, real-world deployment on a resource-constrained edge computer further achieves an approximately stable inference latency of $\\sim$5.3 ms, avoiding the latency spikes and freezing events observed with planning baselines.","short_abstract":"Autonomous navigation in dense and highly dynamic environments requires both physically feasible control and low-latency replanning. Optimization-based methods such as Model Predictive Control (MPC) explicitly handle robot kinematics and safety constraints, but repeated nonlinear optimization can limit real-time respon...","url_abs":"https://arxiv.org/abs/2607.10288","url_pdf":"https://arxiv.org/pdf/2607.10288v1","authors":"[\"Shibo Li\",\"Zhongcheng Wang\",\"Jiahe Cao\",\"Jianhua Yang\",\"Ke Wu\"]","published":"2026-07-11T12:34:28Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[\"Diffusion Model\"]","has_code":false}