University of Southern California（南加州大学）

Closed-Loop Bayesian Bandit Encoder with GRAND Receiver for a Bursty Interference Channel

用于突发干扰信道的带GRAND接收机的闭环贝叶斯智能体编码器

Bhaskar Krishnamachari

机构 * Ming Hsieh Department of Electrical and Computer Engineering（明希学校电气与计算机工程系） ； Viterbi School of Engineering, University of Southern California（维特比工程学院，南加州大学） 纠错

AI总结 研究在有未知干扰源的信道上，随机线性码与跨码字交织码的分组级选择。采用GRAND接收机及贝叶斯估计器，通过折扣汤普森采样器选传输模式，经实验得出不同阶段模式偏好及模型优势，还给出误块率降低等成果。

Comments 11 pages, 6 figures

CLAM: Continuous Latent Action Models for Robot Learning from Unlabeled Demonstrations

CLAM：基于未标记演示的连续潜在动作模型用于机器人学习

Anthony Liang, Pavel Czempin, Matthew M. Hong, Yutai Zhou, Jingzhen Wang, Erdem Biyik, Stephen Tu

机构 * Department of Computer Science, University of Southern California（南加州大学计算机科学系） ； Department of Electrical and Computer Engineering, University of Southern California（南加州大学电气与计算机工程系） 纠错

AI总结 CLAM通过连续潜在动作标签和联合训练动作解码器，有效解决复杂连续控制任务中未标记数据的学习问题，实现在DMControl和MetaWorld等基准及真实机器人上的性能提升。

Comments Latent Action Models, Self-supervised Pretraining, Learning from Videos

Journal ref IEEE/RSJ International Conference on Intelligent Robots and Systems 2026