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Itai Segall

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

2 author rows

IROS Conference 2021 Conference Paper

daß: Distributable And Scalable Simulation of Robotic Applications

Hans C. Woithe
Itai Segall

Simulation is an essential tool for developing robotic systems; however it is computationally intensive and does not scale well, especially for multi-robot scenarios. In this paper we introduce daß – a system that facilitates distributable and scalable simulations of robotic applications. It overcomes many of the performance and quality limitations involved in common multi-robot simulation scenarios through horizontal scaling. Through extensive evaluation, we show that daß significantly outperforms the widely used Gazebo robotics simulator, and easily scales well beyond the current limitations with no significant loss in performance and simulation fidelity.

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ICRA Conference 2021 Conference Paper

The Resh Programming Language for Multirobot Orchestration

Martin Carroll
Kedar S. Namjoshi
Itai Segall

This paper describes Resh, a new, statically typed, interpreted programming language and associated runtime for orchestrating multirobot systems. The main features of Resh are: (1) It offloads much of the tedious work of programming such systems away from the programmer and into the language runtime; (2) It is based on a small set of temporal and locational operators; and (3) It is not restricted to specific robot types or tasks. The Resh runtime consists of three engines that collaborate to run a Resh program using the available robots in their current environment. This paper describes both Resh and its runtime and gives examples of its use.

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AAAI Conference 2019 Conference Paper

Verifying Robustness of Gradient Boosted Models

Gil Einziger
Maayan Goldstein
Yaniv Sa’ar
Itai Segall

Gradient boosted models are a fundamental machine learning technique. Robustness to small perturbations of the input is an important quality measure for machine learning models, but the literature lacks a method to prove the robustness of gradient boosted models. This work introduces VERIGB, a tool for quantifying the robustness of gradient boosted models. VERIGB encodes the model and the robustness property as an SMT formula, which enables state of the art verification tools to prove the model’s robustness. We extensively evaluate VERIGB on publicly available datasets and demonstrate a capability for verifying large models. Finally, we show that some model configurations tend to be inherently more robust than others.

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