118 citations · 135 across the 15 of their papers we have counts for
16 papers
Decentralized and Asymmetric Multi-Agent Learning in Construction Sites
Yakov Miron, Dan Navon, Yuval Goldfracht +2
Multi-agent collaboration involves multiple participants working together in a shared environment to achieve a common goal. These agents share information, divide tasks, and synchr…
CONFIDE: Contextual Finite Differences Modelling of PDEs
Ori Linial, Orly Avner, Dotan Di Castro
We introduce a method for inferring an explicit PDE from a data sample generated by previously unseen dynamics, based on a learned context. The training phase integrates knowledge…
InsertionNet 2.0: Minimal Contact Multi-Step Insertion Using Multimodal Multiview Sensory Input
Oren Spector, Vladimir Tchuiev, Dotan Di Castro
We address the problem of devising the means for a robot to rapidly and safely learn insertion skills with just a few human interventions and without hand-crafted rewards or demons…
BIDCD -- Bosch Industrial Depth Completion Dataset
Adam Botach, Yuri Feldman, Yakov Miron +2
We introduce BIDCD -- the Bosch Industrial Depth Completion Dataset. BIDCD is a new RGBD dataset of metallic industrial objects, collected with a depth camera mounted on a robotic…
Vision-Guided Forecasting -- Visual Context for Multi-Horizon Time Series Forecasting
Eitan Kosman, Dotan Di Castro
Autonomous driving gained huge traction in recent years, due to its potential to change the way we commute. Much effort has been put into trying to estimate the state of a vehicle.…
SOLO: Search Online, Learn Offline for Combinatorial Optimization Problems
Joel Oren, Chana Ross, Maksym Lefarov +5
We study combinatorial problems with real world applications such as machine scheduling, routing, and assignment. We propose a method that combines Reinforcement Learning (RL) and…
InsertionNet -- A Scalable Solution for Insertion
Oren Spector, Dotan Di Castro
Complicated assembly processes can be described as a sequence of two main activities: grasping and insertion. While general grasping solutions are common in industry, insertion is…
Depth Completion with RGB Prior
Yuri Feldman, Yoel Shapiro, Dotan Di Castro
Depth cameras are a prominent perception system for robotics, especially when operating in natural unstructured environments. Industrial applications, however, typically involve re…
Practical Risk Measures in Reinforcement Learning
Dotan Di Castro, Joel Oren, Shie Mannor
Practical application of Reinforcement Learning (RL) often involves risk considerations. We study a generalized approximation scheme for risk measures, based on Monte-Carlo simulat…
One-Shot Session Recommendation Systems with Combinatorial Items
Yahel David, Dotan Di Castro, Zohar Karnin
In recent years, content recommendation systems in large websites (or \emph{content providers}) capture an increased focus. While the type of content varies, e.g.\ movies, articles…
Contextual Markov Decision Processes
Assaf Hallak, Dotan Di Castro, Shie Mannor
We consider a planning problem where the dynamics and rewards of the environment depend on a hidden static parameter referred to as the context. The objective is to learn a strateg…
Policy Evaluation with Variance Related Risk Criteria in Markov Decision Processes
Aviv Tamar, Dotan Di Castro, Shie Mannor
In this paper we extend temporal difference policy evaluation algorithms to performance criteria that include the variance of the cumulative reward. Such criteria are useful for ri…
Policy Gradients with Variance Related Risk Criteria
Dotan Di Castro, Aviv Tamar, Shie Mannor
Managing risk in dynamic decision problems is of cardinal importance in many fields such as finance and process control. The most common approach to defining risk is through variou…
Bandits with an Edge
Dotan Di Castro, Claudio Gentile, Shie Mannor
We consider a bandit problem over a graph where the rewards are not directly observed. Instead, the decision maker can compare two nodes and receive (stochastic) information pertai…
A Maximal Large Deviation Inequality for Sub-Gaussian Variables
Dotan Di Castro, Claudio Gentile, Shie Mannor
In this short note we prove a maximal concentration lemma for sub-Gaussian random variables stating that for independent sub-Gaussian random variables we have \[P<(\max_{1\le i\le…
Adaptive Bases for Reinforcement Learning
Dotan Di Castro, Shie Mannor
We consider the problem of reinforcement learning using function approximation, where the approximating basis can change dynamically while interacting with the environment. A motiv…