From the 1 of 10 linked papers with an AI index.
10 papers
WALA Learning Executable Latent Actions from Action-Labeled Demonstrations and Action-Free Videos
Jiahao Liu, Zhongpu Xia, Shuai Tian +13
WALA is a framework that learns executable latent actions for robot manipulation by pretraining on both action‑labeled demonstrations and unlabeled videos, predicting future change…
InCoM: Intent-Driven Perception and Structured Coordination for Mobile Manipulation
Jiahao Liu, Cui Wenbo, Zhongpu Xia +3
Mobile manipulation is a fundamental capability for general-purpose robotic agents, requiring both coordinated control of the mobile base and manipulator and robust perception unde…
DreamerAD: Efficient Reinforcement Learning via Latent World Model for Autonomous Driving
Pengxuan Yang, Yupeng Zheng, Deheng Qian +11
We introduce DreamerAD, the first latent world model framework that enables efficient reinforcement learning for autonomous driving by compressing diffusion sampling from 100 steps…
Learning Rollout from Sampling:An R1-Style Tokenized Traffic Simulation Model
Ziyan Wang, Peng Chen, Ding Li +4
Learning diverse and high-fidelity traffic simulations from human driving demonstrations is crucial for autonomous driving evaluation. The recent next-token prediction (NTP) paradi…
PerlAD: Towards Enhanced Closed-loop End-to-end Autonomous Driving with Pseudo-simulation-based Reinforcement Learning
Yinfeng Gao, Qichao Zhang, Deqing Liu +8
End-to-end autonomous driving policies based on Imitation Learning (IL) often struggle in closed-loop execution due to the misalignment between inadequate open-loop training object…
TakeAD: Preference-based Post-optimization for End-to-end Autonomous Driving with Expert Takeover Data
Deqing Liu, Yinfeng Gao, Deheng Qian +9
Existing end-to-end autonomous driving methods typically rely on imitation learning (IL) but face a key challenge: the misalignment between open-loop training and closed-loop deplo…