4 papers
TwinRL: Digital Twin-Driven Reinforcement Learning for Real-World Robotic Manipulation
Qinwen Xu, Jiaming Liu, Rui Zhou +11
Despite strong generalization capabilities, Vision-Language-Action (VLA) models remain constrained by the high cost of expert demonstrations and limited real-world interaction. Whi…
HarmoWAM: Harmonizing Generalizable and Precise Manipulation via Adaptive World Action Models
Qiuxuan Feng, Jiale Yu, Jiaming Liu +8
World Action Models (WAMs) have emerged as a promising paradigm for robot control by modeling physical dynamics. Current WAMs generally follow two paradigms: the "Imagine-then-Exec…
Look Before Acting: Enhancing Vision Foundation Representations for Vision-Language-Action Models
Yulin Luo, Hao Chen, Zhuangzhe Wu +10
Vision-Language-Action (VLA) models have recently emerged as a promising paradigm for robotic manipulation, in which reliable action prediction critically depends on accurately int…
S2-Track: A Simple yet Strong Approach for End-to-End 3D Multi-Object Tracking
Tao Tang, Lijun Zhou, Pengkun Hao +9
3D multiple object tracking (MOT) plays a crucial role in autonomous driving perception. Recent end-to-end query-based trackers simultaneously detect and track objects, which have…