26 citations · 39 across the 3 of their papers we have counts for
5 papers
Helpful DoggyBot: Open-World Object Fetching using Legged Robots and Vision-Language Models
Qi Wu, Zipeng Fu, Xuxin Cheng +2
Learning-based methods have achieved strong performance for quadrupedal locomotion. However, several challenges prevent quadrupeds from learning helpful indoor skills that require…
Deep Whole-Body Control: Learning a Unified Policy for Manipulation and Locomotion
Zipeng Fu, Xuxin Cheng, Deepak Pathak
An attached arm can significantly increase the applicability of legged robots to several mobile manipulation tasks that are not possible for the wheeled or tracked counterparts. Th…
Reinforcement Learning for Robust Parameterized Locomotion Control of Bipedal Robots
Zhongyu Li, Xuxin Cheng, Xue Bin Peng +4
Developing robust walking controllers for bipedal robots is a challenging endeavor. Traditional model-based locomotion controllers require simplifying assumptions and careful model…
Automated Lane Change Strategy using Proximal Policy Optimization-based Deep Reinforcement Learning
Fei Ye, Xuxin Cheng, Pin Wang +2
Lane-change maneuvers are commonly executed by drivers to follow a certain routing plan, overtake a slower vehicle, adapt to a merging lane ahead, etc. However, improper lane chang…
Driving Decision and Control for Autonomous Lane Change based on Deep Reinforcement Learning
Tianyu Shi, Pin Wang, Xuxin Cheng +2
We apply Deep Q-network (DQN) with the consideration of safety during the task for deciding whether to conduct the maneuver. Furthermore, we design two similar Deep Q learning fram…