# Motion Planning and Control for Mobile Robot Navigation Using Machine Learning: a Survey

arXiv: https://arxiv.org/abs/2011.13112 · HTML: https://mlsanity.com/paper/2011.13112/

## Key facts

- Cited by 58 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2020.

## Built on by (most cited first, 30 of 58)

- [Principles and Guidelines for Evaluating Social Robot Navigation Algorithms](https://mlsanity.com/paper/2306.16740/) (2023)
- [VLM-Social-Nav: Socially Aware Robot Navigation through Scoring using Vision-Language Models](https://mlsanity.com/paper/2404.00210/) (2024)
- [Multi-Robot Motion Planning with Diffusion Models](https://mlsanity.com/paper/2410.03072/) (2024)
- [No Regrets: Investigating and Improving Regret Approximations for Curriculum Discovery](https://mlsanity.com/paper/2408.15099/) (2024)
- [BEHAVIOR Robot Suite: Streamlining Real-World Whole-Body Manipulation for Everyday Household Activities](https://mlsanity.com/paper/2503.05652/) (2025)
- [Learning Model Predictive Controllers with Real-Time Attention for Real-World Navigation](https://mlsanity.com/paper/2209.10780/) (2022)
- [Can We Trust Embodied Agents? Exploring Backdoor Attacks against Embodied LLM-based Decision-Making Systems](https://mlsanity.com/paper/2405.20774/) (2024)
- [Benchmarking Reinforcement Learning Techniques for Autonomous Navigation](https://mlsanity.com/paper/2210.04839/) (2022)
- [LDP: A Local Diffusion Planner for Efficient Robot Navigation and Collision Avoidance](https://mlsanity.com/paper/2407.01950/) (2024)
- [Contrastive Learning for Enhancing Robust Scene Transfer in Vision-based Agile Flight](https://mlsanity.com/paper/2309.09865/) (2023)
- [Conflict Avoidance in Social Navigation -- a Survey](https://mlsanity.com/paper/2106.12113/) (2021)
- [DTG : Diffusion-based Trajectory Generation for Mapless Global Navigation](https://mlsanity.com/paper/2403.09900/) (2024)
- [MLANet: Multi-Level Attention Network with Sub-instruction for Continuous Vision-and-Language Navigation](https://mlsanity.com/paper/2303.01396/) (2023)
- [STERLING: Self-Supervised Terrain Representation Learning from Unconstrained Robot Experience](https://mlsanity.com/paper/2309.15302/) (2023)
- [Discrete-Guided Diffusion for Scalable and Safe Multi-Robot Motion Planning](https://mlsanity.com/paper/2508.20095/) (2025)
- [Generative Adversarial Super-Resolution at the Edge with Knowledge Distillation](https://mlsanity.com/paper/2209.03355/) (2022)
- [Wait, That Feels Familiar: Learning to Extrapolate Human Preferences for Preference Aligned Path Planning](https://mlsanity.com/paper/2309.09912/) (2023)
- [PathRL: An End-to-End Path Generation Method for Collision Avoidance via Deep Reinforcement Learning](https://mlsanity.com/paper/2310.13295/) (2023)
- [Parameterized Decision-making with Multi-modal Perception for Autonomous Driving](https://mlsanity.com/paper/2312.11935/) (2023)
- [Reinforcement Learning for Wheeled Mobility on Vertically Challenging Terrain](https://mlsanity.com/paper/2409.02383/) (2024)
- [Reinforcement Learning in Practice: Opportunities and Challenges](https://mlsanity.com/paper/2202.11296/) (2022)
- [Autonomous Driving Small-Scale Cars: A Survey of Recent Development](https://mlsanity.com/paper/2404.06229/) (2024)
- [A Reinforcement Learning-Boosted Motion Planning Framework: Comprehensive Generalization Performance in Autonomous Driving](https://mlsanity.com/paper/2402.01465/) (2024)
- [VANP: Learning Where to See for Navigation with Self-Supervised Vision-Action Pre-Training](https://mlsanity.com/paper/2403.08109/) (2024)
- [Grounded Curriculum Learning](https://mlsanity.com/paper/2409.19816/) (2024)
- [PACER: Preference-conditioned All-terrain Costmap Generation](https://mlsanity.com/paper/2410.23488/) (2024)
- [Transforming a Quadruped into a Guide Robot for the Visually Impaired: Formalizing Wayfinding, Interaction Modeling, and Safety Mechanism](https://mlsanity.com/paper/2306.14055/) (2023)
- [A Study on Learning Social Robot Navigation with Multimodal Perception](https://mlsanity.com/paper/2309.12568/) (2023)
- [Optimistic critics can empower small actors](https://mlsanity.com/paper/2506.01016/) (2025)
- [Arena-Rosnav 2.0: A Development and Benchmarking Platform for Robot Navigation in Highly Dynamic Environments](https://mlsanity.com/paper/2302.10023/) (2023)

## Abstract (arXiv)

Moving in complex environments is an essential capability of intelligent mobile robots. Decades of research and engineering have been dedicated to developing sophisticated navigation systems to move mobile robots from one point to another. Despite their overall success, a recently emerging research thrust is devoted to developing machine learning techniques to address the same problem, based in large part on the success of deep learning. However, to date, there has not been much direct comparison between the classical and emerging paradigms to this problem. In this article, we survey recent works that apply machine learning for motion planning and control in mobile robot navigation, within the context of classical navigation systems. The surveyed works are classified into different categories, which delineate the relationship of the learning approaches to classical methods. Based on this classification, we identify common challenges and promising future directions.