# HumanDiffusion: A Vision-Based Diffusion Trajectory Planner with Human-Conditioned Goals for Search and Rescue UAV

arXiv: https://arxiv.org/abs/2601.14973 · HTML: https://mlsanity.com/paper/2601.14973/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 32 references that are in the map.
- First posted on arXiv 21 Jan 2026.

## Builds on (most cited first, 11 of 11)

- [Omni-Scale Feature Learning for Person Re-Identification](https://mlsanity.com/paper/1905.00953/) (2019)
- [NoMaD: Goal Masked Diffusion Policies for Navigation and Exploration](https://mlsanity.com/paper/2310.07896/) (2023)
- [EGO-Planner: An ESDF-free Gradient-based Local Planner for Quadrotors](https://mlsanity.com/paper/2008.08835/) (2020)
- [Diffusion Models for Robotic Manipulation: A Survey](https://mlsanity.com/paper/2504.08438/) (2025)
- [UAV-VLA: Vision-Language-Action System for Large Scale Aerial Mission Generation](https://mlsanity.com/paper/2501.05014/) (2025)
- [DiPPeR: Diffusion-based 2D Path Planner applied on Legged Robots](https://mlsanity.com/paper/2310.07842/) (2023)
- [Grounded Vision-Language Navigation for UAVs with Open-Vocabulary Goal Understanding](https://mlsanity.com/paper/2506.10756/) (2025)
- [EmbodiedDiffusion: End-to-End Traversability-Guided Visual Diffusion for Heterogeneous Robot Navigation](https://mlsanity.com/paper/2512.02851/) (2025)
- [A Search and Detection Autonomous Drone System: from Design to Implementation](https://mlsanity.com/paper/2211.15866/) (2022)
- [FollowMe: a Robust Person Following Framework Based on Re-Identification and Gestures](https://mlsanity.com/paper/2311.12992/) (2023)
- [An MPC framework for efficient navigation of mobile robots in cluttered environments](https://mlsanity.com/paper/2509.15917/) (2025)

## Built on by (most cited first, 1 of 1)

- [HRIBench: Benchmarking Interaction-Centric Human-Robot Collaboration](https://mlsanity.com/paper/2607.13056/) (2026)

## Abstract (arXiv)

Reliable human--robot collaboration in emergency scenarios requires autonomous systems that can detect humans, infer navigation goals, and operate safely in dynamic environments. This paper presents HumanDiffusion, a lightweight image-conditioned diffusion planner that generates human-aware navigation trajectories directly from RGB imagery. The system combines YOLO-11 based human detection with diffusion-driven trajectory generation, enabling a quadrotor to approach a target person and deliver medical assistance without relying on prior maps or computationally intensive planning pipelines. Trajectories are predicted in pixel space, ensuring smooth motion and a consistent safety margin around humans. We evaluate HumanDiffusion in simulation and real-world indoor mock-disaster scenarios. On a 300-sample test set, the model achieves a mean squared error of 0.02 in pixel-space trajectory reconstruction. Real-world experiments demonstrate an overall mission success rate of 80% across accident-response and search-and-locate tasks with partial occlusions. These results indicate that human-conditioned diffusion planning offers a practical and robust solution for human-aware UAV navigation in time-critical assistance settings.