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Whole-Body Safe Control of Robotic Systems with Koopman Neural Dynamics
Sebin Jung, Abulikemu Abuduweili, Jiaxing Li +1
Controlling robots with strongly nonlinear, high-dimensional dynamics remains challenging, as direct nonlinear optimization with safety constraints is often intractable in real tim…
Scaling Law of Neural Koopman Operators
Abulikemu Abuduweili, Yuyang Pang, Feihan Li +1
Data-driven neural Koopman operator theory has emerged as a powerful tool for linearizing and controlling nonlinear robotic systems. However, the performance of these data-driven m…
Trends in Motion Prediction Toward Deployable and Generalizable Autonomy: A Revisit and Perspectives
Letian Wang, Marc-Antoine Lavoie, Sandro Papais +13
Motion prediction, recently popularized as world models, refers to the anticipation of future agent states or scene evolution, which is rooted in human cognition, bridging percepti…
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…
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…