3 citations · 3 across the 7 of their papers we have counts for
7 papers
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…
SPARK: Safe Protective and Assistive Robot Kit
Yifan Sun, Rui Chen, Kai S. Yun +6
This paper introduces the Safe Protective and Assistive Robot Kit (SPARK), a comprehensive benchmark designed to ensure safety in humanoid autonomy and teleoperation. Humanoid robo…
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…
Absolute State-wise Constrained Policy Optimization: High-Probability State-wise Constraints Satisfaction
Weiye Zhao, Feihan Li, Yifan Sun +4
Enforcing state-wise safety constraints is critical for the application of reinforcement learning (RL) in real-world problems, such as autonomous driving and robot manipulation. Ho…
Implicit Safe Set Algorithm for Provably Safe Reinforcement Learning
Weiye Zhao, Feihan Li, Changliu Liu
Deep reinforcement learning (DRL) has demonstrated remarkable performance in many continuous control tasks. However, a significant obstacle to the real-world application of DRL is…
Absolute Policy Optimization
Weiye Zhao, Feihan Li, Yifan Sun +3
In recent years, trust region on-policy reinforcement learning has achieved impressive results in addressing complex control tasks and gaming scenarios. However, contemporary state…
State-wise Constrained Policy Optimization
Weiye Zhao, Rui Chen, Yifan Sun +2
Reinforcement Learning (RL) algorithms have shown tremendous success in simulation environments, but their application to real-world problems faces significant challenges, with saf…