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Chana Ross

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3 papers
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3

IROS Conference 2022 Conference Paper

Towards Autonomous Grading In The Real World

  • Yakov Miron
  • Chana Ross
  • Yuval Goldfracht
  • Chen Tessler
  • Dotan Di Castro

Surface grading is an integral part of the construction pipeline. Here, a bulldozer, which is a key machinery tool at any construction site, is required to level an uneven area containing pre-dumped sand piles. In this work, we aim to tackle the problem of autonomous surface grading on real-world scenarios. We design both a realistic physical simulation and a scaled real-world prototype environment mimicking real bulldozer dynamics and sensory information. In addition, we establish heuristics and learning strategies in order to solve the problem. Through extensive experiments, we show that although heuristics are capable of tackling the problem in a clean and noise-free simulated environment, they fail catastrophically when facing real-world scenarios. However, we show that the simulation can be leveraged to guide a learning agent, which can generalize and solve the task both in simulation and in a scaled prototype environment.

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PRL Workshop 2021 Workshop Paper

SOLO: Search Online, Learn Offline for Combinatorial Optimization Problems

  • Joel Oren
  • Chana Ross
  • Maksym Lefarov
  • Felix Richter
  • Ayal Taitler
  • Zohar Feldman Zohar Feldman
  • Dotan Di Castro
  • Christian Daniel

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 planning. This method can equally be applied to both the offline, as well as online, variants of the combinatorial problem, in which the problem components (e. g. , jobs in scheduling problems) are not known in advance, but rather arrive during the decision-making process. Our solution is quite generic, scalable, and leverages distributional knowledge of the problem parameters. We frame the solution process as an MDP, and take a Deep Q-Learning approach wherein states are represented as graphs, thereby allowing our trained policies to deal with arbitrary changes in a principled manner. Though learned policies work well in expectation, small deviations can have substantial negative effects in combinatorial settings. We mitigate these drawbacks by employing our graph-convolutional policies as non-optimal heuristics in a compatible search algorithm, Monte Carlo Tree Search, to significantly improve overall performance. We demonstrate our method on two problems: Machine Scheduling and Capacitated Vehicle Routing. We show that our method outperforms custom-tailored mathematical solvers, state of the art learning-based algorithms, and common heuristics, both in computation time and performance.

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SoCS Conference 2021 Conference Paper

SOLO: Search Online, Learn Offline for Combinatorial Optimization Problems

  • Joel Oren
  • Chana Ross
  • Maksym Lefarov
  • Felix Richter 0001
  • Ayal Taitler
  • Zohar Feldman
  • Dotan Di Castro
  • Christian G. Daniel

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 planning. This method can equally be applied to both the offline, as well as online, variants of the combinatorial problem, in which the problem components (e. g. , jobs in scheduling problems) are not known in advance, but rather arrive during the decision-making process. Our solution is quite generic, scalable, and leverages distributional knowledge of the problem parameters. We frame the solution process as an MDP, and take a Deep Q-Learning approach wherein states are represented as graphs, thereby allowing our trained policies to deal with arbitrary changes in a principled manner. Though learned policies work well in expectation, small deviations can have substantial negative effects in combinatorial settings. We mitigate these drawbacks by employing our graph-convolutional policies as non-optimal heuristics in a compatible search algorithm, Monte Carlo Tree Search, to significantly improve overall performance. We demonstrate our method on two problems: Machine Scheduling and Capacitated Vehicle Routing. We show that our method outperforms custom-tailored mathematical solvers, state of the art learning-based algorithms, and common heuristics, both in computation time and performance.

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