机器人 / 具身智能

Physically-Grounded Goal Imagination: Physics-Informed Variational Autoencoder for Self-Supervised Reinforcement Learning

Lan Thi Ha Nguyen, Kien Ton Manh, Anh Do Duc, Nam Pham Hai

机构 * FPT University（FPT大学） 纠错

专题命中 模仿学习与强化学习 ：manipulation(abstract)；robotic(abstract)；分类 cs.RO、cs.AI

Improving Robustness to Out-of-Distribution States in Imitation Learning via Deep Koopman-Boosted Diffusion Policy

Dianye Huang, Nassir Navab, Zhongliang Jiang

机构 * Chair for Computer Aided Medical Procedures and Augmented Reality (CAMP), Technical University of Munich (TUM)（计算机辅助医疗程序与增强现实中心（CAMP），慕尼黑技术大学（TUM）） ； Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心） ； Medical Intelligence and Robotic Cognition Lab (MIRoC), The University of Hong Kong (HKU)（医疗智能与机器人认知实验室（MIRoC），香港大学（HKU）） 纠错

专题命中 模仿学习与强化学习 ：manipulation(abstract)；robotic(abstract)；分类 cs.RO

Comments Accepted by IEEE T-RO

Learning to Focus: Prioritizing Informative Histories with Structured Attention Mechanisms in Partially Observable Reinforcement Learning

Daniel De Dios Allegue, Jinke He, Frans A. Oliehoek

机构 * Delft University of Technology（代尔夫特理工大学） 纠错

专题命中 模仿学习与强化学习 ：world model(abstract,comments)；分类 cs.AI、cs.LG

Comments Accepted to Embodied World Models for Decision Making (EWM) Workshop at NeurIPS 2025

Dynamics-Decoupled Trajectory Alignment for Sim-to-Real Transfer in Reinforcement Learning for Autonomous Driving

Thomas Steinecker, Alexander Bienemann, Denis Trescher, Thorsten Luettel, Mirko Maehlisch

机构 * University of the Bundeswehr Munich（联邦国防军慕尼黑大学） 纠错

专题命中 模仿学习与强化学习 ：robotics(abstract)；分类 cs.RO、cs.LG

MrCoM: A Meta-Regularized World-Model Generalizing Across Multi-Scenarios

Xuantang Xiong, Ni Mu, Runpeng Xie, Senhao Yang, Yaqing Wang, Lexiang Wang, Yao Luan, Siyuan Li, Shuang Xu, Yiqin Yang, Bo Xu

专题命中 模仿学习与强化学习 ：world model(abstract)；分类 cs.AI、cs.LG

Improving Deepfake Detection with Reinforcement Learning-Based Adaptive Data Augmentation

Yuxuan Zhou, Tao Yu, Wen Huang, Yuheng Zhang, Tao Dai, Shu-Tao Xia

专题命中 模仿学习与强化学习 ：manipulation(abstract)；分类 cs.CV