9 citations · 9 across the 11 of their papers we have counts for
13 papers · 1 filter
Learning Highly Dynamic Skills Transition for Quadruped Jumping Through Constrained Space
Zeren Luo, Jiahui Zhang, Yimin Han +4
Although legged animals are capable of performing explosive motions while traversing confined spaces, replicating this behavior in quadrupedal robots has been a longstanding challe…
MILD: Tractable Terrain Modeling for Learning Improved Bipedal Locomotion on Deformable Surfaces
Zeren Luo, Jiahui Zhang, Zhe Xu +6
Enabling robots to walk on yielding terrain is vital for applications ranging from disaster response to planetary exploration. While bipedal robots hold immense potential, their lo…
RoboNaldo: Accurate, Stable and Powerful Humanoid Soccer Shooting via Motion-Guided Curriculum Reinforcement Learning
Yichao Zhong, Yidan Lu, Yuhang Lu +9
Elite humanoid soccer shooting requires whole-body stability, high-impulse whole-body interactions, and accuracy to targets. Motion tracking-driven reinforcement learning (RL) prov…
Unified Walking, Running, and Recovery for Humanoids via State-Dependent Adversarial Motion Priors
Yidan Lu, Yichao Zhong, Liu Zhao +2
We propose a unified reinforcement learning framework that enables a single policy to perform walking, running, and fall recovery on the Unitree G1 humanoid robot, validated on phy…
SMASH: Mastering Scalable Whole-Body Skills for Humanoid Ping-Pong with Egocentric Vision
Junli Ren, Yinghui Li, Kai Zhang +12
Existing humanoid table tennis systems remain limited by their reliance on external sensing and their inability to achieve agile whole-body coordination for precise task execution.…
Co-jump: Cooperative Jumping with Quadrupedal Robots via Multi-Agent Reinforcement Learning
Shihao Dong, Yeke Chen, Zeren Luo +8
While single-agent legged locomotion has witnessed remarkable progress, individual robots remain fundamentally constrained by physical actuation limits. To transcend these boundari…
Learning Human-Like Badminton Skills for Humanoid Robots
Yeke Chen, Shihao Dong, Xiaoyu Ji +12
Realizing versatile and human-like performance in high-demand sports like badminton remains a formidable challenge for humanoid robotics. Unlike standard locomotion or static manip…
Like Playing a Video Game: Spatial-Temporal Optimization of Foot Trajectories for Controlled Football Kicking in Bipedal Robots
Wanyue Li, Ji Ma, Minghao Lu +1
Humanoid robot soccer presents several challenges, particularly in maintaining system stability during aggressive kicking motions while achieving precise ball trajectory control. C…
MARG: MAstering Risky Gap Terrains for Legged Robots with Elevation Mapping
Yinzhao Dong, Ji Ma, Liu Zhao +2
Deep Reinforcement Learning (DRL) controllers for quadrupedal locomotion have demonstrated impressive performance on challenging terrains, allowing robots to execute complex skills…
Contrastive Representation Learning for Robust Sim-to-Real Transfer of Adaptive Humanoid Locomotion
Yidan Lu, Rurui Yang, Qiran Kou +5
Reinforcement learning has produced remarkable advances in humanoid locomotion, yet a fundamental dilemma persists for real-world deployment: policies must choose between the robus…
FR-Net: Learning Robust Quadrupedal Fall Recovery on Challenging Terrains through Mass-Contact Prediction
Yidan Lu, Yinzhao Dong, Jiahui Zhang +2
Fall recovery for legged robots remains challenging, particularly on complex terrains where traditional controllers fail due to incomplete terrain perception and uncertain interact…
In-between Motion Generation Based Multi-Style Quadruped Robot Locomotion
Yuanhao Chen, Liu Zhao, Ji Ma +1
Quadruped robots face persistent challenges in achieving versatile locomotion due to limitations in reference motion data diversity. To address these challenges, we introduce an in…
Learning an Adaptive Fall Recovery Controller for Quadrupeds on Complex Terrains
Yidan Lu, Yinzhao Dong, Ji Ma +2
Legged robots have shown promise in locomotion complex environments, but recovery from falls on challenging terrains remains a significant hurdle. This paper presents an Adaptive F…