6 papers
From Noise to Intent: Anchoring Generative VLA Policies with Residual Bridges
Yiming Zhong, Yaoyu He, Zemin Yang +5
Bridging high-level semantic understanding with low-level physical control remains a persistent challenge in embodied intelligence, stemming from the fundamental spatiotemporal sca…
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…
Implicit Drifting Policy: One-Step Action Generation via Conditional Expert Geometry
Zemin Yang, Yaoyu He, Yiming Zhong +5
Generative action policies based on diffusion or flow matching excel in behavior cloning, yet their iterative sampling is prohibitive for high-frequency robot control. While recent…
Data-Asymmetric Latent Imagination and Reranking for 3D Robotic Imitation Learning
Lianghao Luo, Xizhou Bu, Ruyan Liu +5
Robotic imitation learning typically assumes access to optimal demonstrations, yet real-world data collection often yields suboptimal, exploratory, or even failed trajectories. Dis…
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…