PIER-Flow: Physics-Informed Efficient Rectified Flow for Real-Time Mobile Robot Navigation

Jul 14, 2026

Researchers introduce PIER-Flow, a lightweight navigation policy for mobile robots that distills a Model Predictive Control (MPC) expert into a continuous-time ODE, enabling single-step action generation. In simulation, PIER-Flow achieves a 98.85% success rate with zero collisions and an average inference time of approximately 1.29 ms, representing a 37.2× speedup over MPC and over 800× over standard diffusion models. Real-world deployment on edge hardware demonstrates stable inference latency of around 5.3 ms, outperforming planning baselines in responsiveness and reliability.

Why it matters: PIER-Flow demonstrates a significant advance in enabling real-time, collision-free navigation for mobile robots in dense environments, particularly on resource-constrained hardware.

Full story at: arXiv Robotics ↗