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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…
Bayesian Relational Memory for Semantic Visual Navigation
Yi Wu, Yuxin Wu, Aviv Tamar +3
We introduce a new memory architecture, Bayesian Relational Memory (BRM), to improve the generalization ability for semantic visual navigation agents in unseen environments, where…
Sub-Goal Trees -- a Framework for Goal-Directed Trajectory Prediction and Optimization
Tom Jurgenson, Edward Groshev, Aviv Tamar
Many AI problems, in robotics and other domains, are goal-directed, essentially seeking a trajectory leading to some goal state. In such problems, the way we choose to represent a…
Harnessing Reinforcement Learning for Neural Motion Planning
Tom Jurgenson, Aviv Tamar
Motion planning is an essential component in most of today's robotic applications. In this work, we consider the learning setting, where a set of solved motion planning problems is…
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…
Reinforcement Learning on Variable Impedance Controller for High-Precision Robotic Assembly
Jianlan Luo, Eugen Solowjow, Chengtao Wen +4
Precise robotic manipulation skills are desirable in many industrial settings, reinforcement learning (RL) methods hold the promise of acquiring these skills autonomously. In this…
Domain Randomization for Active Pose Estimation
Xinyi Ren, Jianlan Luo, Eugen Solowjow +4
Accurate state estimation is a fundamental component of robotic control. In robotic manipulation tasks, as is our focus in this work, state estimation is essential for identifying…
A Risk-Sensitive Finite-Time Reachability Approach for Safety of Stochastic Dynamic Systems
Margaret P. Chapman, Jonathan Lacotte, Aviv Tamar +7
A classic reachability problem for safety of dynamic systems is to compute the set of initial states from which the state trajectory is guaranteed to stay inside a given constraint…
Multi-Agent Reinforcement Learning with Multi-Step Generative Models
Orr Krupnik, Igor Mordatch, Aviv Tamar
We consider model-based reinforcement learning (MBRL) in 2-agent, high-fidelity continuous control problems -- an important domain for robots interacting with other agents in the s…