4 papers
What Matters for Latent Actions in Robot Learning
Xizhou Bu, Qingda Hu, Lei Zhou +13
Latent Action Models (LAMs) have emerged as a promising paradigm for enabling robot learning to leverage large-scale unlabeled videos through latent actions that serve as compact s…
LAFP: Preserving Latent Action Structure in Latent Policy Learning via Flow Matching
Jiexi Lyu, Xizhou Bu, Qingqiu Huang +4
Learning high-quality latent actions from large-scale unlabeled videos, coupled with limited real-world interaction data for training an action decoder, has emerged as a promising…
Percept-WAM: Perception-Enhanced World-Awareness-Action Model for Robust End-to-End Autonomous Driving
Jianhua Han, Meng Tian, Jiangtong Zhu +16
Autonomous driving heavily relies on accurate and robust spatial perception. Many failures arise from inaccuracies and instability, especially in long-tail scenarios and complex in…
STAGE: A Stream-Centric Generative World Model for Long-Horizon Driving-Scene Simulation
Jiamin Wang, Yichen Yao, Xiang Feng +5
The generation of temporally consistent, high-fidelity driving videos over extended horizons presents a fundamental challenge in autonomous driving world modeling. Existing approac…