University of Twente Student Theses
Path Planning with DDPMs
Nikken, M.S. (2024) Path Planning with DDPMs.
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| Abstract: | Denoising Diffusion Probabilistic Models (DDPMs) are powerful generative deep learning models that have been very successful at image generation. In this paper, we investigate how to leverage the generalization and conditional-sampling capabilities of DDPMs to generate complex paths for a robotic end effector. We show that these paths can exhibit obstacle avoidance, even when training the DDPM on synthetically generated low-quality demonstrations. The trained DDPM is deployed in a receding-horizon control scheme to enhance its planning capabilities. The Denoising Diffusion Planner is experimentally validated through various experiments on a Franka Emika Panda robot. |
| Item Type: | Essay (Master) |
| Faculty: | EEMCS: Electrical Engineering, Mathematics and Computer Science |
| Subject: | 50 technical science in general |
| Programme: | Robotics MSc (60973) |
| Link to this item: | https://purl.utwente.nl/essays/104342 |
| Export this item as: | BibTeX EndNote HTML Citation Reference Manager |
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