# DiPPeST: Diffusion-based Path Planner for Synthesizing Trajectories Applied on Quadruped Robots

arXiv: https://arxiv.org/abs/2405.19232 · HTML: https://mlsanity.com/paper/2405.19232/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 May 2024.

## Built on by (most cited first, 10 of 10)

- [DiffusionDrive: Truncated Diffusion Model for End-to-End Autonomous Driving](https://mlsanity.com/paper/2411.15139/) (2024)
- [Motion Planning Diffusion: Learning and Adapting Robot Motion Planning with Diffusion Models](https://mlsanity.com/paper/2412.19948/) (2024)
- [M2Diffuser: Diffusion-based Trajectory Optimization for Mobile Manipulation in 3D Scenes](https://mlsanity.com/paper/2410.11402/) (2024)
- [Diffusion Model for Planning: A Systematic Literature Review](https://mlsanity.com/paper/2408.10266/) (2024)
- [Generative AI Agents in Autonomous Machines: A Safety Perspective](https://mlsanity.com/paper/2410.15489/) (2024)
- [LiteVLoc: Map-Lite Visual Localization for Image Goal Navigation](https://mlsanity.com/paper/2410.04419/) (2024)
- [Watch Your STEPP: Semantic Traversability Estimation using Pose Projected Features](https://mlsanity.com/paper/2501.17594/) (2025)
- [COLSON: Controllable Learning-Based Social Navigation via Diffusion-Based Reinforcement Learning](https://mlsanity.com/paper/2503.13934/) (2025)
- [Beyond Imitation: Reinforcement Learning Fine-Tuning for Adaptive Diffusion Navigation Policies](https://mlsanity.com/paper/2603.12868/) (2026)
- [MulDP: Multimodal Diffusion Policy for Autonomous Quadruped Parkour Navigation across Complex Terrains](https://mlsanity.com/paper/2609.03984/) (2026)

## Abstract (arXiv)

We present DiPPeST, a novel image and goal conditioned diffusion-based trajectory generator for quadrupedal robot path planning. DiPPeST is a zero-shot adaptation of our previously introduced diffusion-based 2D global trajectory generator (DiPPeR). The introduced system incorporates a novel strategy for local real-time path refinements, that is reactive to camera input, without requiring any further training, image processing, or environment interpretation techniques. DiPPeST achieves 92% success rate in obstacle avoidance for nominal environments and an average of 88% success rate when tested in environments that are up to 3.5 times more complex in pixel variation than DiPPeR. A visual-servoing framework is developed to allow for real-world execution, tested on the quadruped robot, achieving 80% success rate in different environments and showcasing improved behavior than complex state-of-the-art local planners, in narrow environments.