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Enhancing Sample Generation of Diffusion Models using Noise Level Correction
Abulikemu Abuduweili, Chenyang Yuan, Changliu Liu +1
The denoising process of diffusion models can be interpreted as an approximate projection of noisy samples onto the data manifold. Moreover, the noise level in these samples approx…
Revisiting the Initial Steps in Adaptive Gradient Descent Optimization
Abulikemu Abuduweili, Changliu Liu
Adaptive gradient optimization methods, such as Adam, are prevalent in training deep neural networks across diverse machine learning tasks due to their ability to achieve faster co…
Robustifying Long-term Human-Robot Collaboration through a Multimodal and Hierarchical Framework
Peiqi Yu, Abulikemu Abuduweili, Ruixuan Liu +1
Long-term Human-Robot Collaboration (HRC) is crucial for enabling flexible manufacturing systems and integrating companion robots into daily human environments over extended period…
Continual Learning and Lifting of Koopman Dynamics for Linear Control of Legged Robots
Feihan Li, Abulikemu Abuduweili, Yifan Sun +3
The control of legged robots, particularly humanoid and quadruped robots, presents significant challenges due to their high-dimensional and nonlinear dynamics. While linear systems…
Estimating Neural Network Robustness via Lipschitz Constant and Architecture Sensitivity
Abulikemu Abuduweili, Changliu Liu
Ensuring neural network robustness is essential for the safe and reliable operation of robotic learning systems, especially in perception and decision-making tasks within real-worl…
KOROL: Learning Visualizable Object Feature with Koopman Operator Rollout for Manipulation
Hongyi Chen, Abulikemu Abuduweili, Aviral Agrawal +4
Learning dexterous manipulation skills presents significant challenges due to complex nonlinear dynamics that underlie the interactions between objects and multi-fingered hands. Ko…