9 citations · 9 across the 10 of their papers we have counts for
10 papers
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…
Hyperbolic Hierarchical Alignment Reasoning Network for Text-3D Retrieval
Wenrui Li, Yidan Lu, Yeyu Chai +3
With the daily influx of 3D data on the internet, text-3D retrieval has gained increasing attention. However, current methods face two major challenges: Hierarchy Representation Co…
Switch-JustDance: Benchmarking Whole Body Motion Tracking Controllers Using a Commercial Console Game
Jeonghwan Kim, Wontaek Kim, Yidan Lu +9
Recent advances in whole-body robot control have enabled humanoid and legged robots to perform increasingly agile and coordinated motions. However, standardized benchmarks for eval…
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…
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…
Digging into Intrinsic Contextual Information for High-fidelity 3D Point Cloud Completion
Jisheng Chu, Wenrui Li, Xingtao Wang +3
The common occurrence of occlusion-induced incompleteness in point clouds has made point cloud completion (PCC) a highly-concerned task in the field of geometric processing. Existi…
Riemann-based Multi-scale Attention Reasoning Network for Text-3D Retrieval
Wenrui Li, Wei Han, Yandu Chen +4
Due to the challenges in acquiring paired Text-3D data and the inherent irregularity of 3D data structures, combined representation learning of 3D point clouds and text remains une…
Rethinking Dense Retrieval's Few-Shot Ability
Si Sun, Yida Lu, Shi Yu +6
Few-shot dense retrieval (DR) aims to effectively generalize to novel search scenarios by learning a few samples. Despite its importance, there is little study on specialized datas…