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Kush Grover

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5 papers

2 author rows

AAMAS Conference 2026 Conference Paper

Resilient Strategies for Stochastic Systems: How Much Does it Take to Break a Winning Strategy?

Kush Grover
Markel Zubia
Debraj Chakraborty
Muqsit Azeem
Nils Jansen
Jan Kretinský

We study the problem of resilient strategies in the presence of uncertainty. Resilient strategies enable an agent to make decisions that are robust against disturbances. In particular, we are interested in those disturbances that are able to flip a decision made by the agent. Such a disturbance may, for instance, occur when the intended action of the agent cannot be executed due to a malfunction of an actuator in the environment. In this work, we introduce the concept of resilience in the stochastic setting and present a comprehensive set of fundamental problems. Specifically, we address these problems for Markov decision processes with reachability and safety objectives, which also smoothly extend to stochastic games. We provide various ways of aggregating the amounts of disturbances that may have occurred, for instance, in expectation or in the worst case. Moreover, to reason about infinite disturbances, we use quantitative measures, like their frequency of occurrence.

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ICRA Conference 2022 Conference Paper

Planning via model checking with decision-tree controllers

Jonis Kiesbye
Kush Grover
Pranav Ashok
Jan Kretínský

Planning problems can be solved not only by planners, but also by model checkers. While the former yield a plan that requires replanning as soon as any fault occurs, the latter provide a “universal” plan (a. k. a. strategy, policy, or controller) able to make decisions under all circumstances. One of the prohibitive aspects of the latter approach is stemming from this very advantage: since it is defined for all possible states of the system, it is typically so large that it does not fit into small memories of embedded devices. As another consequence of the size, its execution may be slow. In this paper, we provide a solution to this issue by linking the model checkers with decision-tree learners, resulting in decision-tree representations of the synthesized strategies. Not only are they dramatically smaller, but also more explainable and orders-of-magnitude faster to execute than plans with replanning. In addition, we describe a method for model validation and debugging via the model checker and the decision-tree learner in the loop. We illustrate the approach on our case study of a robotic arm for picking items in a real industrial setting.

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Highlights Conference 2022 Conference Abstract

Planning Via Model Checking with Decision-Tree Controllers

Kush Grover

Planning problems can be solved not only by planners but also by model checkers. While the former yield a plan that requires replanning as soon as any fault occurs, the latter provides a “universal” plan (a. k. a. strategy, policy, or controller) able to make decisions under all circumstances. One of the prohibitive aspects of the latter approach is stemming from this very advantage: since it is defined for all possible states of the system, it is typically so large that it does not fit into small memories of embedded devices. As another consequence of the size, its execution may be slow. In this paper, we provide a solution to this issue by linking the model checkers with decision-tree learners, resulting in decision-tree representations of the synthesized strategies. Not only are they dramatically smaller, but also more explainable and orders-of-magnitude faster to execute than plans with replanning. In addition, we describe a method for model validation and debugging via the model checker and the decision-tree learner in the loop. We illustrate the approach on our case study of a robotic arm for picking items in a real industrial setting. Joint work with Jonis Kiesbye, Pranav Ashok, Jan Kretinsky.

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Highlights Conference 2021 Conference Abstract

Semantic Abstraction-Guided Motion Planning for scLTL Missions in Unknown Environments

Kush Grover

Complex mission specifications for robots can be often specified through temporal logics, such as Linear Temporal Logic and its syntactically co-safe fragment, scLTL. Finding trajectories that satisfy such specifications becomes hard if the robot is to fulfil the mission in an initially unknown environment, where neither locations of regions or objects of interest in the environment nor the obstacle space are known a priori. We propose an algorithm that, while exploring the environment, learns important semantic dependencies in the form of a semantic abstraction, and uses it to bias the growth of an RRG graph towards faster mission completion. Our approach leads to finding trajectories that are much shorter than those found by the sequential approach, which first explores and then plans. Simulations comparing our solution to the sequential approach, carried out in 100 randomized office-like environments, show more than 50% reduction in the trajectory length. This work was done in collaboration with F. Barbosa, J. Tumova, and J. Kretinsky.

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Highlights Conference 2020 Conference Abstract

An Anytime Algorithm for Reachability on Uncountable MDP

Kush Grover

We provide an algorithm for reachability on Markov decision processes with uncountable state and action spaces, which, under mild assumptions, approximates the optimal value to any desired precision. It is the first such anytime algorithm, meaning that at any point in time it can return the current approximation with its precision. Moreover, it simultaneously is the first algorithm able to utilize learning approaches without sacrificing guarantees and it further allows for combination with existing heuristics.

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