5 accepted papers
We propose a new approach to solving the problem of intention estimation in human-robot teleoperation for assembly tasks, which includes task estimation and action prediction. Our approach uses probabilistic graphical models to represent the joint distribution of the task and the actions to be taken
We address the problem of intention estimation in human-robot teleoperation, which involves identifying the task being completed and predicting the next actions. Our approach sequentially quantifies the similarity between the observed action sequence and nominal action sequences representing possibl
Timely capturing the dense geometry of the surrounding scene with unlabeled LiDAR data is valuable but under-explored for mobile robotic applications. Its value lies in the huge amount of such unlabeled data, enabling self-supervised learning for various downstream tasks. Current dynamic 3D scene re…
The driver warning system that alerts the human driver about potential risks during driving is a key feature of an advanced driver assistance system. Existing driver warning technologies, mainly the forward collision warning and unsafe lane change warning, can reduce the risk of collision caused by…
We derive upper bounds on the generalization error of a learning algorithm in terms of the mutual information between its input and output. The bounds provide an information-theoretic understanding of generalization in learning problems, and give theoretical guidelines for striking the right balance…