6 papers
GaussianSeed: Hierarchical Gaussian Seeding for High-Resolution 3D Occupancy Prediction
Xinzhuo Li, Xianghui Pan, Jiayuan Du +4
Vision-centric 3D occupancy prediction provides dense scene representations essential for autonomous driving and robotic navigation, yet existing methods struggle to scale to high…
MemPose: Category-level Object Pose Estimation with Memory
Xiao Lin, Minghao Zhu, Yun Peng +4
In the pursuit of robust and generalizable category-level object pose estimation, most existing methods adopt parametric formulations that learn effective representations from data…
Kinematics-Aware Multi-Policy Reinforcement Learning for Force-Capable Humanoid Loco-Manipulation
Kaiyan Xiao, Zihan Xu, Cheng Zhe +2
Humanoid robots, with their human-like morphology, hold great potential for industrial applications. However, existing loco-manipulation methods primarily focus on dexterous manipu…
Meta-Learning Adaptive Loss Functions
Christian Raymond, Qi Chen, Bing Xue +1
Loss function learning is a new meta-learning paradigm that aims to automate the essential task of designing a loss function for a machine learning model. Existing techniques for l…
Realizing Text-Driven Motion Generation on NAO Robot: A Reinforcement Learning-Optimized Control Pipeline
Zihan Xu, Mengxian Hu, Kaiyan Xiao +3
Human motion retargeting for humanoid robots, transferring human motion data to robots for imitation, presents significant challenges but offers considerable potential for real-wor…
SAM-LAD: Segment Anything Model Meets Zero-Shot Logic Anomaly Detection
Yun Peng, Xiao Lin, Nachuan Ma +4
Visual anomaly detection is vital in real-world applications, such as industrial defect detection and medical diagnosis. However, most existing methods focus on local structural an…