3 citations · 4 across the 4 of their papers we have counts for
5 papers · 1 filter
Can World and Action Modeling Benefit Each Other under Unified Causal Training?
Yue Su, Bokai Lin, Xinkai Wang +9
World and action modeling are closely related, yet their reciprocal benefits under unified causal training remain unclear. We introduce OneWM, a simple framework for unified causal…
ObjDream: Guiding Vision-Language-Action Models with Object-Level Understanding and Future State Prediction
Huailiang Ma, Yuxuan Li, Ziyi Zhao +7
Vision-language-action (VLA) models have emerged as a powerful paradigm for robotic manipulation, enabling robots to generate actions from visual observations and language instruct…
ForceWAM: Decoupling Future and Response Learning from Demo and Play Data for Contact-Rich Manipulation
Zijun Xu, Wendi Chen, Xinkai Wang +16
Expert demonstrations show how to complete contact-rich tasks, but provide limited coverage of how interaction forces respond to different actions. Task-agnostic play provides broa…
Track4Action: Distilling World-Centric 3D Tracker into Vision-Language-Action Policies
Chenyi Wang, Xinkai Wang, Bokai Lin +4
Action labels tell a vision-language-action (VLA) policy which robot commands to imitate, but not how those commands change the 3D world. The aligned demonstration clip contains th…
LaMP: Learning Vision-Language-Action Policy with 3D Scene Flow as Latent Motion Prior
Xinkai Wang, Chenyi Wang, Yifu Xu +7
We introduce \textbf{LaMP}, a dual-expert Vision-Language-Action framework that embeds dense 3D scene flow as a latent motion prior for robotic manipulation.Existing VLA models reg…