261 citations · 465 across the 10 of their papers we have counts for
3 papers · 1 filter
Deep Variational Semi-Supervised Novelty Detection
Tal Daniel, Thanard Kurutach, Aviv Tamar
In anomaly detection (AD), one seeks to identify whether a test sample is abnormal, given a data set of normal samples. A recent and promising approach to AD relies on deep generat…
Learning to Manipulate Deformable Objects without Demonstrations
Yilin Wu, Wilson Yan, Thanard Kurutach +2
In this paper we tackle the problem of deformable object manipulation through model-free visual reinforcement learning (RL). In order to circumvent the sample inefficiency of RL, w…
Learning Robotic Manipulation through Visual Planning and Acting
Angelina Wang, Thanard Kurutach, Kara Liu +2
Planning for robotic manipulation requires reasoning about the changes a robot can affect on objects. When such interactions can be modelled analytically, as in domains with rigid…