9 papers
DRAM: Delta-rule Recurrent Associative Memory for Robot Manipulation Policies
Xinyu Zhao, Yixiang Shan, Tao Yang +5
Robotic manipulation is inherently history-dependent, yet most pretrained robotic policies condition on only the current observation or a short temporal window. Equipping such poli…
Decoupling Policy Extraction for Offline Reinforcement Learning
Xuyao Lin, Yixiang Shan, Jinru Duan +7
Offline RL methods commonly jointly train the actor and critic, where the critic is used to guide the actor toward higher-value actions. This coupled learning process is well motiv…
Rethink Before You Execute: Adaptive Execution for World Action Models
Feng Ye, Yiming Zhao, Yong Yu +5
World Action Models (WAMs) jointly predict future actions and the evolution of the environment. At each inference, a WAM generates a chunk of actions and the robot executes a fixed…
LaST-HD: Learning Latent Physical Reasoning from Scalable Human Data for Robot Manipulation
Jiaming Liu, Yinxi Wang, Chenyang Gu +15
Human-hand demonstrations provide a direct and scalable source of physical interaction data for robot learning. While manual retargeting is indispensable for establishing kinematic…
LaST-R1: Reinforcing Robotic Manipulation via Adaptive Physical Latent Reasoning
Hao Chen, Jiaming Liu, Zhonghao Yan +10
Robotic foundation models require reasoning over complex visual scenes to execute adaptive actions in dynamic environments. While recent studies on latent-reasoning Vision-Language…
GeoDrive: 3D Geometry-Informed Driving World Model with Precise Action Control
Anthony Chen, Wenzhao Zheng, Yida Wang +5
Recent advancements in world models have revolutionized dynamic environment simulation, allowing systems to foresee future states and assess potential actions. In autonomous drivin…
StyledStreets: Multi-style Street Simulator with Spatial and Temporal Consistency
Yuyin Chen, Yida Wang, Xueyang Zhang +4
Urban scene reconstruction requires modeling both static infrastructure and dynamic elements while supporting diverse environmental conditions. We present \textbf{StyledStreets}, a…
GaussianAD: Gaussian-Centric End-to-End Autonomous Driving
Wenzhao Zheng, Junjie Wu, Yao Zheng +8
Vision-based autonomous driving shows great potential due to its satisfactory performance and low costs. Most existing methods adopt dense representations (e.g., bird's eye view) o…
ReconDreamer: Crafting World Models for Driving Scene Reconstruction via Online Restoration
Chaojun Ni, Guosheng Zhao, Xiaofeng Wang +13
Closed-loop simulation is crucial for end-to-end autonomous driving. Existing sensor simulation methods (e.g., NeRF and 3DGS) reconstruct driving scenes based on conditions that cl…