Liu, J;
Stamatopoulou, M;
Kanoulas, D;
(2024)
DiPPeR: Diffusion-based 2D Path Planner applied on Legged Robots.
In:
Proceedings - IEEE International Conference on Robotics and Automation.
(pp. pp. 9264-9270).
IEEE
Preview |
Text
Diffusion_2D_planner_ICRA2024.pdf - Published Version Download (9MB) | Preview |
Abstract
In this work, we present DiPPeR, a novel and fast 2D path planning framework for quadrupedal locomotion, leveraging diffusion-driven techniques. Our contributions include a scalable dataset generator for map images and corresponding trajectories, an image-conditioned diffusion planner for mobile robots, and a training/inference pipeline employing CNNs. We validate our approach in several mazes, as well as in real-world deployment scenarios on Boston Dynamic's Spot and Unitree's Go1 robots. DiPPeR performs on average 23 times faster for trajectory generation against both search based and data driven path planning algorithms with an average of 87% consistency in producing feasible paths of various length in maps of variable size, and obstacle structure. Website: https://rpl-cs-ucl.github.io/DiPPeR/
| Type: | Proceedings paper |
|---|---|
| Title: | DiPPeR: Diffusion-based 2D Path Planner applied on Legged Robots |
| Event: | 2024 IEEE International Conference on Robotics and Automation (ICRA) |
| Dates: | 13 May 2024 - 17 May 2024 |
| ISBN-13: | 979-8-3503-8457-4 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/ICRA57147.2024.10610013 |
| Publisher version: | https://doi.org/10.1109/ICRA57147.2024.10610013 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Training, Legged locomotion, Heuristic algorithms, Pipelines, Network architecture, Transformers, Generators |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10197016 |
Archive Staff Only
 |
View Item |
