7 papers
Triplet2Track: A Hierarchical System with Object-Centric Representations for Reliable Long-Horizon Manipulation
Jianxiang Liu, Gaojing Zhang, Chuan Wen +4
Ensuring reliability in uncertain environments remains difficult for long-horizon robotic manipulation. End-to-end VLA models are data-heavy and opaque, making diagnosis and verifi…
Practice Makes Policies: Bootstrapping and Consolidating Robotic Capabilities from Zero Human Demonstrations
Jialiang Li, Yuhan Wang, Haojun Li +5
General-purpose robotic manipulation requires robots to perform diverse tasks in open-world environments while improving their skills over time. Despite recent progress in robotic…
HALOMI: Learning Humanoid Loco-Manipulation with Active Perception from Human Demonstrations
Zehui Zhao, Yuxuan Zhao, Gaojing Zhang +3
Human demonstrations, which can be collected at scale and naturally capture active hand-eye coordination, are a promising data source for learning humanoid loco-manipulation. Howev…
FSAG: Enhancing Human-to-Dexterous-Hand Finger-Specific Affordance Grounding via Diffusion Models
Yifan Han, Yichuan Peng, Pengfei Yi +5
Dexterous grasp synthesis must jointly satisfy functional intent and physical feasibility, yet existing pipelines often decouple semantic grounding from refinement, yielding unstab…
SAGE: Scene Graph-Aware Guidance and Execution for Long-Horizon Manipulation Tasks
Jialiang Li, Wenzheng Wu, Gaojing Zhang +2
Successfully solving long-horizon manipulation tasks remains a fundamental challenge. These tasks involve extended action sequences and complex object interactions, presenting a cr…
LLGS: Unsupervised Gaussian Splatting for Image Enhancement and Reconstruction in Pure Dark Environment
Haoran Wang, Jingwei Huang, Lu Yang +3
3D Gaussian Splatting has shown remarkable capabilities in novel view rendering tasks and exhibits significant potential for multi-view optimization.However, the original 3D Gaussi…
LTCF-Net: A Transformer-Enhanced Dual-Channel Fourier Framework for Low-Light Image Restoration
Gaojing Zhang, Jinglun Feng
We introduce LTCF-Net, a novel network architecture designed for enhancing low-light images. Unlike Retinex-based methods, our approach utilizes two color spaces - LAB and YUV - to…