# LDP: A Local Diffusion Planner for Efficient Robot Navigation and Collision Avoidance

arXiv: https://arxiv.org/abs/2407.01950 · HTML: https://mlsanity.com/paper/2407.01950/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Placed in Diffusion and flow policies for robotics, one of 14 directions in Diffusion models.
- Builds on 23 of its 32 references that are in the map.
- First posted on arXiv 2 Jul 2024.

## Placed in

- Diffusion models / [Diffusion and flow policies for robotics](https://mlsanity.com/diffusion/diffusion-and-flow-policies-for-robotics/)

## Builds on (most cited first, 23 of 23)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow](https://mlsanity.com/paper/2209.03003/) (2022)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://mlsanity.com/paper/2303.04137/) (2023)
- [Consistency Models](https://mlsanity.com/paper/2303.01469/) (2023)
- [Decision Transformer: Reinforcement Learning via Sequence Modeling](https://mlsanity.com/paper/2106.01345/) (2021)
- [Planning with Diffusion for Flexible Behavior Synthesis](https://mlsanity.com/paper/2205.09991/) (2022)
- [End to End Learning for Self-Driving Cars](https://mlsanity.com/paper/1604.07316/) (2016)
- [What Matters in Learning from Offline Human Demonstrations for Robot Manipulation](https://mlsanity.com/paper/2108.03298/) (2021)
- [Implicit Behavioral Cloning](https://mlsanity.com/paper/2109.00137/) (2021)
- [Universal Manipulation Interface: In-The-Wild Robot Teaching Without In-The-Wild Robots](https://mlsanity.com/paper/2402.10329/) (2024)
- [NoMaD: Goal Masked Diffusion Policies for Navigation and Exploration](https://mlsanity.com/paper/2310.07896/) (2023)
- [Motion Planning Diffusion: Learning and Planning of Robot Motions with Diffusion Models](https://mlsanity.com/paper/2308.01557/) (2023)
- [Diffusion Model is an Effective Planner and Data Synthesizer for Multi-Task Reinforcement Learning](https://mlsanity.com/paper/2305.18459/) (2023)
- [From Play to Policy: Conditional Behavior Generation from Uncurated Robot Data](https://mlsanity.com/paper/2210.10047/) (2022)
- [Diffusion Models for Reinforcement Learning: A Survey](https://mlsanity.com/paper/2311.01223/) (2023)
- [Motion Planning and Control for Mobile Robot Navigation Using Machine Learning: a Survey](https://mlsanity.com/paper/2011.13112/) (2020)
- [Success Weighted by Completion Time: A Dynamics-Aware Evaluation Criteria for Embodied Navigation](https://mlsanity.com/paper/2103.08022/) (2021)
- [Crowd-Aware Robot Navigation for Pedestrians with Multiple Collision Avoidance Strategies via Map-based Deep Reinforcement Learning](https://mlsanity.com/paper/2109.02541/) (2021)
- [Learning to Socially Navigate in Pedestrian-rich Environments with Interaction Capacity](https://mlsanity.com/paper/2203.16154/) (2022)
- [PathRL: An End-to-End Path Generation Method for Collision Avoidance via Deep Reinforcement Learning](https://mlsanity.com/paper/2310.13295/) (2023)
- [P3O: Transferring Visual Representations for Reinforcement Learning via Prompting](https://mlsanity.com/paper/2303.12371/) (2023)

## Built on by (most cited first, 18 of 18)

- [DiffusionDrive: Truncated Diffusion Model for End-to-End Autonomous Driving](https://mlsanity.com/paper/2411.15139/) (2024)
- [M2Diffuser: Diffusion-based Trajectory Optimization for Mobile Manipulation in 3D Scenes](https://mlsanity.com/paper/2410.11402/) (2024)
- [DreamNav: A Trajectory-Based Imaginative Framework for Zero-Shot Vision-and-Language Navigation](https://mlsanity.com/paper/2509.11197/) (2025)
- [DARE: Diffusion Policy for Autonomous Robot Exploration](https://mlsanity.com/paper/2410.16687/) (2024)
- [VDT-Auto: End-to-end Autonomous Driving with VLM-Guided Diffusion Transformers](https://mlsanity.com/paper/2502.20108/) (2025)
- [Constraint-Aware Diffusion Guidance for Robotics: Real-Time Obstacle Avoidance for Autonomous Racing](https://mlsanity.com/paper/2505.13131/) (2025)
- [DAgger Diffusion Navigation: DAgger Boosted Diffusion Policy for Vision-Language Navigation](https://mlsanity.com/paper/2508.09444/) (2025)
- [PANDORA: Diffusion Policy Learning for Dexterous Robotic Piano Playing](https://mlsanity.com/paper/2503.14545/) (2025)
- [Self-Imitated Diffusion Policy for Efficient and Robust Visual Navigation](https://mlsanity.com/paper/2601.22965/) (2026)
- [CAC-VLA: Context-Gated Action Conditioning for Vision-Language-Action Models](https://mlsanity.com/paper/2607.04816/) (2026)
- [Perception Helps Planning: Facilitating Multi-Stage Lane-Level Integration via Double-Edge Structures](https://mlsanity.com/paper/2407.11644/) (2024)
- [Deep Active Inference with Diffusion Policy and Multiple Timescale World Model for Real-World Exploration and Navigation](https://mlsanity.com/paper/2510.23258/) (2025)
- [CAPE: Context-Aware Diffusion Policy Via Proximal Mode Expansion for Collision Avoidance](https://mlsanity.com/paper/2511.22773/) (2025)
- [ProDapt: Proprioceptive Adaptation using Long-term Memory Diffusion](https://mlsanity.com/paper/2503.00193/) (2025)
- [PIER-Flow: Physics-Informed Efficient Rectified Flow for Real-Time Mobile Robot Navigation](https://mlsanity.com/paper/2607.10288/) (2026)
- [HyperDCM: Dynamic Cluster Memory Replay in Hyperbolic Space for Continual Robotic Navigation Across Scenes](https://mlsanity.com/paper/2607.16267/) (2026)
- [MulDP: Multimodal Diffusion Policy for Autonomous Quadruped Parkour Navigation across Complex Terrains](https://mlsanity.com/paper/2609.03984/) (2026)
- [SoGuDiff: Socially Guided Diffusion for Steerable, Norm-Grounded Robot Navigation](https://mlsanity.com/paper/2609.30560/) (2026)

## Abstract (arXiv)

The conditional diffusion model has been demonstrated as an efficient tool for learning robot policies, owing to its advancement to accurately model the conditional distribution of policies. The intricate nature of real-world scenarios, characterized by dynamic obstacles and maze-like structures, underscores the complexity of robot local navigation decision-making as a conditional distribution problem. Nevertheless, leveraging the diffusion model for robot local navigation is not trivial and encounters several under-explored challenges: (1) Data Urgency. The complex conditional distribution in local navigation needs training data to include diverse policy in diverse real-world scenarios; (2) Myopic Observation. Due to the diversity of the perception scenarios, diffusion decisions based on the local perspective of robots may prove suboptimal for completing the entire task, as they often lack foresight. In certain scenarios requiring detours, the robot may become trapped. To address these issues, our approach begins with an exploration of a diverse data generation mechanism that encompasses multiple agents exhibiting distinct preferences through target selection informed by integrated global-local insights. Then, based on this diverse training data, a diffusion agent is obtained, capable of excellent collision avoidance in diverse scenarios. Subsequently, we augment our Local Diffusion Planner, also known as LDP by incorporating global observations in a lightweight manner. This enhancement broadens the observational scope of LDP, effectively mitigating the risk of becoming ensnared in local optima and promoting more robust navigational decisions.