5 papers
Humanoid Badminton: Learning Dynamic Racket Skills from Limited Human Motion Data
Jingzhi Cui, Zhexiong Wang, Bangjie Xu +9
High-speed racket sports provide a demanding testbed for humanoid robots, requiring time-critical decisions, precise striking, and dynamic whole-body coordination. In badminton, fa…
Imagine then Verify: Affordance-Targeted Active Perception for Task-Oriented Grasping in Cluttered Scenes
Jingzhi Cui, Xuefeng Liu, Feng Han +3
Task-oriented grasping (TOG) requires robots to grasp functional parts of objects (e.g., the handle of a mug for pouring), yet these affordance regions are frequently occluded in c…
Weave: Learning Whole-Body Dexterous Loco-Manipulation from Human-Object Interactions
Liu Cao, Xingze Wu, Jingzhi Cui +4
Learning humanoid-object interaction requires coordinating whole-body balance, locomotion, and dexterous hand contact to control both robot and object motion. Human demonstrations…
GigaBrain-0.7: Scaling Embodied Foundation Models to Emergent Capabilities with a Three-System Architecture
GigaBrain Team, Angen Ye, Axiang Sun +56
Vision-language-action (VLA) models have become a dominant paradigm for generalist embodied agents, demonstrating strong complex and long-horizon task completion in structured sett…
DuQuant: Distributing Outliers via Dual Transformation Makes Stronger Quantized LLMs
Haokun Lin, Haobo Xu, Yichen Wu +6
Quantization of large language models (LLMs) faces significant challenges, particularly due to the presence of outlier activations that impede efficient low-bit representation. Tra…