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NeuPAN: Direct Point Robot Navigation with End-to-End Model-based Learning

Ruihua Han, Shuai Wang, Shuaijun Wang, Zeqing Zhang, Jianjun Chen, Shijie Lin, Chengyang Li, Chengzhong Xu, Yonina C. Eldar, Qi Hao, Jia Pan

TL;DR

NeuPAN tackles dense-scenario, mapless navigation for nonholonomic robots by directly mapping raw lidar points to a latent distance feature space and solving an end-to-end perception-control problem with a tightly coupled PAN-based framework. The system combines a Deep Unfolded Neural Encoder (DUNE) that outputs latent distance features with a Neural Regularized Motion Planner (NRMP) that uses a differentiable, learnable optimization layer to generate safe, efficient motions within a receding horizon $H$. Theoretical grounding via strong duality transforms pointwise collision constraints into a convex-regularized objective, enabling real-time performance and interpretability, while experimental results across ground, wheel-legged, and passenger-vehicle platforms show improved accuracy, robustness, and generalization over state-of-the-art baselines. NeuPAN’s end-to-end, model-based learning paradigm yields perception-aware, physically interpretable motions suitable for cluttered, unknown environments, offering practical impact for autonomous systems in home, office, and urban contexts.

Abstract

Navigating a nonholonomic robot in a cluttered, unknown environment requires accurate perception and precise motion control for real-time collision avoidance. This paper presents NeuPAN: a real-time, highly accurate, map-free, easy-to-deploy, and environment-invariant robot motion planner. Leveraging a tightly coupled perception-to-control framework, NeuPAN has two key innovations compared to existing approaches: 1) it directly maps raw point cloud data to a latent distance feature space for collision-free motion generation, avoiding error propagation from the perception to control pipeline; 2) it is interpretable from an end-to-end model-based learning perspective. The crux of NeuPAN is solving an end-to-end mathematical model with numerous point-level constraints using a plug-and-play (PnP) proximal alternating-minimization network (PAN), incorporating neurons in the loop. This allows NeuPAN to generate real-time, physically interpretable motions. It seamlessly integrates data and knowledge engines, and its network parameters can be fine-tuned via backpropagation. We evaluate NeuPAN on a ground mobile robot, a wheel-legged robot, and an autonomous vehicle, in extensive simulated and real-world environments. Results demonstrate that NeuPAN outperforms existing baselines in terms of accuracy, efficiency, robustness, and generalization capabilities across various environments, including the cluttered sandbox, office, corridor, and parking lot. We show that NeuPAN works well in unknown and unstructured environments with arbitrarily shaped objects, transforming impassable paths into passable ones.

NeuPAN: Direct Point Robot Navigation with End-to-End Model-based Learning

TL;DR

NeuPAN tackles dense-scenario, mapless navigation for nonholonomic robots by directly mapping raw lidar points to a latent distance feature space and solving an end-to-end perception-control problem with a tightly coupled PAN-based framework. The system combines a Deep Unfolded Neural Encoder (DUNE) that outputs latent distance features with a Neural Regularized Motion Planner (NRMP) that uses a differentiable, learnable optimization layer to generate safe, efficient motions within a receding horizon . Theoretical grounding via strong duality transforms pointwise collision constraints into a convex-regularized objective, enabling real-time performance and interpretability, while experimental results across ground, wheel-legged, and passenger-vehicle platforms show improved accuracy, robustness, and generalization over state-of-the-art baselines. NeuPAN’s end-to-end, model-based learning paradigm yields perception-aware, physically interpretable motions suitable for cluttered, unknown environments, offering practical impact for autonomous systems in home, office, and urban contexts.

Abstract

Navigating a nonholonomic robot in a cluttered, unknown environment requires accurate perception and precise motion control for real-time collision avoidance. This paper presents NeuPAN: a real-time, highly accurate, map-free, easy-to-deploy, and environment-invariant robot motion planner. Leveraging a tightly coupled perception-to-control framework, NeuPAN has two key innovations compared to existing approaches: 1) it directly maps raw point cloud data to a latent distance feature space for collision-free motion generation, avoiding error propagation from the perception to control pipeline; 2) it is interpretable from an end-to-end model-based learning perspective. The crux of NeuPAN is solving an end-to-end mathematical model with numerous point-level constraints using a plug-and-play (PnP) proximal alternating-minimization network (PAN), incorporating neurons in the loop. This allows NeuPAN to generate real-time, physically interpretable motions. It seamlessly integrates data and knowledge engines, and its network parameters can be fine-tuned via backpropagation. We evaluate NeuPAN on a ground mobile robot, a wheel-legged robot, and an autonomous vehicle, in extensive simulated and real-world environments. Results demonstrate that NeuPAN outperforms existing baselines in terms of accuracy, efficiency, robustness, and generalization capabilities across various environments, including the cluttered sandbox, office, corridor, and parking lot. We show that NeuPAN works well in unknown and unstructured environments with arbitrarily shaped objects, transforming impassable paths into passable ones.
Paper Structure (46 sections, 1 theorem, 53 equations, 24 figures, 7 tables, 3 algorithms)

This paper contains 46 sections, 1 theorem, 53 equations, 24 figures, 7 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Modular vs. End-to-End
  4. Model-based vs. Learning-based
  5. Comprehensive Comparison with Existing Solutions
  6. Problem Statement
  7. NeuPAN System Architecture
  8. Mathematical Interpretation
  9. NeuPAN System
  10. NeuPAN Encoder and Planner Design
  11. Deep Unfolded Neural Encoder
  12. PIBCD for MAMPP Problem
  13. Deep Unfolding Architecture
  14. Loss Function Design
  15. DUNE Training
  16. ...and 31 more sections

Key Result

Theorem 1

The sequence $\left[\{\mathcal{S}^{[0]},\mathcal{U}^{[0]}\},\{\mathcal{S}^{[1]},\mathcal{U}^{[1]}\},\cdots\right]$ satisfies the following conditions: (i) Monotonicity: $C_{\mathrm{e2e}}^{[0]}\geq C_{\mathrm{e2e}}^{[1]}\geq C_{\mathrm{e2e}}^{[2]} \geq\cdots,$ where $C_{\mathrm{e2e}}^{[k]}=C_{\mathrm

Figures (24)

  • Figure 1: Wheel-legged robot mapless navigation in the office empowered by NeuPAN: (a) the robot navigates along a naive path without a prior map; (b) the robot navigates through narrow gaps; ($<6\,$cm); (c) NeuPAN utilizes a "direct point-in action-out" manner at a high frequency; (d) NeuPAN can handle arbitrarily shaped (moving) objects. (e) the robot trajectory.
  • Figure 2: System architecture of NeuPAN, a perception-to-control end-to-end approach for navigation, consists of two main blocks: learning-based and model-based.
  • Figure 3: Geometric interpretation of LDFs $\mathcal{M}$ and $\mathcal{L}$. Red sides represent the closest robot edge(s) to the obstacle point. $\bm{\lambda}_t^i$ represents the normal vector of the separation hyperplane. Elements of $\bm{\mu}_t^i$ with positive value represent the collision related red sides.
  • Figure 4: Relationship between problems $\mathsf{P}$, $\mathsf{Q}$, $\mathsf{Q_1}$, and $\mathsf{Q_2}$
  • Figure 5: Structure of DUNE, which unfolds the PIBCD algorithm. DUNE is explainable, simple, and fast to train, and can be deployed across various scenarios without retraining, provided the robot's shape remains unchanged.
  • ...and 19 more figures

Theorems & Definitions (2)

  • Theorem 1
  • proof