1 citations · 1 across the 12 of their papers we have counts for
13 papers
Trajectory-based Diffusion from One-shot Human Video for Generalizable Manipulation
Guoping Pan, Xinmiao Yu, Wannan Cao +5
Imitation learning enables robots to perform complex tasks by leveraging human-collected demonstrations. However, most existing approaches require labor-intensive data collection w…
Push-Wiper: Toward General-Purpose Robotic Cleaning across Varied Stains and Surfaces with Segmented Pushing Trajectories
Renhao Lu, Mingxin Wang, Chenyang Cao +5
Viscous stains, characterized by high viscosity and complex rheological properties, remain a major challenge for robotic surface cleaning. Conventional wiping often spreads the sta…
Stage-Transition Dense Reward Modeling for Reinforcement Learning
Yang Yang, Bingjie Chen, Zihan Wang +4
Reinforcement learning for long-horizon robotic manipulation is often limited by sparse and delayed rewards, while manually designing dense shaping signals is costly and brittle to…
PhaForce: Phase-Scheduled Visual-Force Policy Learning with Slow Planning and Fast Correction for Contact-Rich Manipulation
Mingxin Wang, Zhirun Yue, Renhao Lu +7
Contact-rich manipulation requires not only vision-dominant task semantics but also closed-loop reactions to force/torque (F/T) transients. Yet, generative visuomotor policies are…
PMG: Parameterized Motion Generator for Human-like Locomotion Control
Chenxi Han, Yuheng Min, Zihao Huang +4
Recent advances in data-driven reinforcement learning and motion tracking have substantially improved humanoid locomotion, yet critical practical challenges remain. In particular,…
A Step Toward World Models: A Survey on Robotic Manipulation
Peng-Fei Zhang, Ying Cheng, Xiaofan Sun +4
Autonomous agents are increasingly expected to operate in complex, dynamic, and uncertain environments, performing tasks such as manipulation, navigation, and decision-making. Achi…
HL-IK: A Lightweight Implementation of Human-Like Inverse Kinematics in Humanoid Arms
Bingjie Chen, Zihan Wang, Zhe Han +3
Traditional IK methods for redundant humanoid manipulators emphasize end-effector (EE) tracking, frequently producing configurations that are valid mechanically but not human-like.…
MBC: Multi-Brain Collaborative Control for Quadruped Robots
Hang Liu, Yi Cheng, Rankun Li +3
In the field of locomotion task of quadruped robots, Blind Policy and Perceptive Policy each have their own advantages and limitations. The Blind Policy relies on preset sensor inf…
CushionCatch: A Compliant Catching Mechanism for Mobile Manipulators via Combined Optimization and Learning
Bingjie Chen, Keyu Fan, Qi Yang +6
Catching flying objects with a cushioning process is a skill commonly performed by humans, yet it remains a significant challenge for robots. In this paper, we present a framework…
Structural Optimization of Lightweight Bipedal Robot via SERL
Yi Cheng, Chenxi Han, Yuheng Min +3
Designing a bipedal robot is a complex and challenging task, especially when dealing with a multitude of structural parameters. Traditional design methods often rely on human intui…
Agile and versatile bipedal robot tracking control through reinforcement learning
Jiayi Li, Linqi Ye, Yi Cheng +2
The remarkable athletic intelligence displayed by humans in complex dynamic movements such as dancing and gymnastics suggests that the balance mechanism in biological beings is dec…
Quadruped robot traversing 3D complex environments with limited perception
Yi Cheng, Hang Liu, Guoping Pan +3
Traversing 3-D complex environments has always been a significant challenge for legged locomotion. Existing methods typically rely on external sensors such as vision and lidar to p…
From Knowing to Doing: Learning Diverse Motor Skills through Instruction Learning
Linqi Ye, Jiayi Li, Yi Cheng +3
Recent years have witnessed many successful trials in the robot learning field. For contact-rich robotic tasks, it is challenging to learn coordinated motor skills by reinforcement…