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Efficient Symbolic Approaches for Quantitative Reactive Synthesis with Finite Tasks (IROS 23)

In this work, we introduce a method of symbolic value iteration for quantitative tow-player games with reachability objectives and with resource constraints.

Karan Muvvala, Morteza Lahijanian

Efficient Symbolic Approaches for Quantitative Reactive Synthesis with Finite Tasks (IROS 23)

Stochastic Games for Interactive Manipulation Domains

In this work, we explore stochastic games as an abstraction for modeling human-robot interaction with stochastic action outcomes and irrational agents. We also improve scalabiltiy of exisitng tool PRISM-games.

Karan Muvvala, Andrew Wells, Morteza Lahijanian, Lydia Kavraki, Moshe Vardi

Stochastic Games for Interactive Manipulation Domains

Safety Guarantees for Neural Network Dynamic Systems via Stochastic Barrier Functions (accepted NeurIPS 22)

In this work, we introduce a method of safety certification and control for neural network dynamic systems via stochastic barrier functions.

Rayan Mazouz, Karan Muvvala, Akash Ratheesh, Luca Laurenti, Morteza Lahijanian

Let's Collaborate: Regret-based Reactive Synthesis for Robotic Manipulation

Synthesizing human-like behavior for robots by reasoning about the human’s action to synthesize smarter interactions.

Karan Muvvala, Peter Amorese, Morteza Lahijanian

Let's Collaborate: Regret-based Reactive Synthesis for Robotic Manipulation

Expert-Informed Autonomous Science Planning for In-situ Observations and Discoveries

Enabling future planetory exploration missions to the icy moons of Jupiter by developing novel, safe and reliable autonomy stack using Formal Methods and Game Theory.

Jay McMahon, Nisar Ahmed, Morteza Lahijanian, Peter Amorese, Taralicin Deka, Karan Muvvala, Trevor Slack, Shohei Wakayama

Expert-Informed Autonomous Science Planning for In-situ Observations and Discoveries