26 citations · 85 across the 11 of their papers we have counts for
11 papers
ExBody2: Advanced Expressive Humanoid Whole-Body Control
Mazeyu Ji, Xuanbin Peng, Fangchen Liu +4
This paper tackles the challenge of enabling real-world humanoid robots to perform expressive and dynamic whole-body motions while maintaining overall stability and robustness. We…
Mobile-TeleVision: Predictive Motion Priors for Humanoid Whole-Body Control
Chenhao Lu, Xuxin Cheng, Jialong Li +6
Humanoid robots require both robust lower-body locomotion and precise upper-body manipulation. While recent Reinforcement Learning (RL) approaches provide whole-body loco-manipulat…
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…
Expressive Whole-Body Control for Humanoid Robots
Xuxin Cheng, Yandong Ji, Junming Chen +3
Can we enable humanoid robots to generate rich, diverse, and expressive motions in the real world? We propose to learn a whole-body control policy on a human-sized robot to mimic h…
Visual Whole-Body Control for Legged Loco-Manipulation
Minghuan Liu, Zixuan Chen, Xuxin Cheng +4
We study the problem of mobile manipulation using legged robots equipped with an arm, namely legged loco-manipulation. The robot legs, while usually utilized for mobility, offer an…
Extreme Parkour with Legged Robots
Xuxin Cheng, Kexin Shi, Ananye Agarwal +1
Humans can perform parkour by traversing obstacles in a highly dynamic fashion requiring precise eye-muscle coordination and movement. Getting robots to do the same task requires o…
Legs as Manipulator: Pushing Quadrupedal Agility Beyond Locomotion
Xuxin Cheng, Ashish Kumar, Deepak Pathak
Locomotion has seen dramatic progress for walking or running across challenging terrains. However, robotic quadrupeds are still far behind their biological counterparts, such as do…
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…