5 papers
MetaTune: Adjoint-based Meta-tuning via Robotic Differentiable Dynamics
Xiexin Peng, Bingheng Wang, Tao Zhang +2
Disturbance observer-based control has shown promise in robustifying robotic systems against uncertainties. However, tuning such systems remains challenging due to the strong coupl…
FUSE: A Framework for Unified State Estimation in Vehicular and Robotic SLAM Systems
Wei Wu, Honglin Chen, Wenhan Cao +7
Tightly coupled SLAM formulations under mixed-rate sensing often bind temporal processing, local geometric association, estimator formulation, and map-update policy into method-spe…
Jump-Start Reinforcement Learning with Self-Evolving Priors for Extreme Monopedal Locomotion
Ziang Zheng, Guojian Zhan, Shiqi Liu +3
Reinforcement learning (RL) has shown great potential in enabling quadruped robots to perform agile locomotion. However, directly training policies to simultaneously handle dual ex…
Transferable Latent-to-Latent Locomotion Policy for Efficient and Versatile Motion Control of Diverse Legged Robots
Ziang Zheng, Guojian Zhan, Bin Shuai +4
Reinforcement learning (RL) has demonstrated remarkable capability in acquiring robot skills, but learning each new skill still requires substantial data collection for training. T…
Zeroth-Order Actor-Critic: An Evolutionary Framework for Sequential Decision Problems
Yuheng Lei, Yao Lyu, Guojian Zhan +5
Evolutionary algorithms (EAs) have shown promise in solving sequential decision problems (SDPs) by simplifying them to static optimization problems and searching for the optimal po…