Ankush Chakrabarty — accepted AI-conference papers

2025

User Preference Meets Pareto-Optimality in Multi-Objective Bayesian Optimization

Joshua Hang Sai Ip, Ankush Chakrabarty, Ali Mesbah, Diego Romeres

AAAI 2025technical

Incorporating user preferences into multi-objective Bayesian optimization (MOBO) allows for personalization of the op- timization procedure. Preferences are often abstracted in the form of an unknown utility function, estimated through pair- wise comparisons of potential outcomes. However, utility-d…

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2022

Safe multi-agent motion planning via filtered reinforcement learning

Abraham P. Vinod, Sleiman Safaoui, Ankush Chakrabarty, Rien Quirynen, Nobuyuki Yoshikawa, Stefano Di Cairano

ICRA 2022poster

We study the problem of safe multi-agent motion planning in cluttered environments. Existing multi-agent reinforcement learning-based motion planners only provide approximate safety enforcement. We propose a safe reinforcement learning algorithm that leverages single-agent reinforcement learning for…

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