Diffusion Models for Robotics
Abstract
Diffusion Models (DMs) offer robust tools for addressing uncertainty and enhancing adaptability in robotics. This work explores their application to trajectory generation, 3D image synthesis, and interpretable scene understanding. For trajectory planning, we propose using colored Gaussian noise to improve robustness and temporal coherence. In 3D image generation, Transfer Entropy enhances information flow between textual and visual modalities for more coherent outputs. Partial Information Decomposition (PID) is leveraged to improve model interpretability and efficiency in scene generation. Rigorous evaluation will assess trajectory quality, robustness, and real-world transferability, aiming to advance autonomous decision-making and scene understanding in robotics.
BibTeX
@article{Liang_2025, title={Diffusion Models for Robotics}, volume={39}, url={https://ojs.aaai.org/index.php/AAAI/article/view/35334}, DOI={10.1609/aaai.v39i28.35334}, abstractNote={Diffusion Models (DMs) offer robust tools for addressing uncertainty and enhancing adaptability in robotics. This work explores their application to trajectory generation, 3D image synthesis, and interpretable scene understanding. For trajectory planning, we propose using colored Gaussian noise to improve robustness and temporal coherence. In 3D image generation, Transfer Entropy enhances information flow between textual and visual modalities for more coherent outputs. Partial Information Decomposition (PID) is leveraged to improve model interpretability and efficiency in scene generation. Rigorous evaluation will assess trajectory quality, robustness, and real-world transferability, aiming to advance autonomous decision-making and scene understanding in robotics.}, number={28}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Liang, Jessica E.}, year={2025}, month={Apr.}, pages={29587-29589} }