5 papers · 1 filter
GE-Act 2.0: Pretraining and Scaling a World-Action Model for Robotic Manipulation
AgiBot Research Team, Renhang Liu, Wenzhi Zhao +42
World-action models (WAM) predict future states to guide robot actions, enabling learning from both action-free video and action-labeled interaction. Most inherit pretrained video…
Genie Sim 3.0 : A High-Fidelity Comprehensive Simulation Platform for Humanoid Robot
Chenghao Yin, Da Huang, Di Yang +16
The development of robust and generalizable robot learning models is critically contingent upon the availability of large-scale, diverse training data and reliable evaluation bench…
Schrödinger's Navigator: Imagining an Ensemble of Futures for Zero-Shot Object Navigation
Yu He, Da Huang, Zhenyang Liu +5
Zero-shot object navigation (ZSON) requires robots to find target objects in unseen environments without task-specific fine-tuning or pre-built maps, a key capability for general-p…
TP-MDDN: Task-Preferenced Multi-Demand-Driven Navigation with Autonomous Decision-Making
Shanshan Li, Da Huang, Yu He +3
In daily life, people often move through spaces to find objects that meet their needs, posing a key challenge in embodied AI. Traditional Demand-Driven Navigation (DDN) handles one…
TrajBooster: Boosting Humanoid Whole-Body Manipulation via Trajectory-Centric Learning
Jiacheng Liu, Pengxiang Ding, Qihang Zhou +8
Recent Vision-Language-Action models show potential to generalize across embodiments but struggle to quickly align with a new robot's action space when high-quality demonstrations…