David Smith Sundarsingh — accepted AI-conference papers

2026

Safe Planning in Unknown Environments Using Conformalized Semantic Maps

David Smith Sundarsingh, Yifei Li, Tianji Tang, George J. Pappas, Nikolay Atanasov, Yiannis Kantaros

RA-L 2026

This paper addresses semantic planning problems in unknown environments under perceptual uncertainty. The environment contains multiple unknown semantically labeled regions or objects, and the robot must reach desired locations while maintaining class-dependent distances from them. We aim to compute

Cited by 1 Source ↗Scholar ↗

2024

Safe Multi-Robot Exploration using Symbolic Control

Manas Sashank Juvvi, David Smith Sundarsingh, Ratnangshu Das, Pushpak Jagtap

ICRA 2024poster

Multi-robot exploration is a complex problem that involves multiple robots working in a shared unknown environment. In such scenarios, the safety of the robots is of paramount importance alongside the completion of the exploration task. In this paper, we propose a modular exploration framework that…

Cited by 0 Source ↗Scholar ↗

2023

Autonomous Exploration Using Ground Robots with Safety Guarantees

David Smith Sundarsingh, Jay Bhagiya, Jeel Chatrola, Pushpak Jagtap

IROS 2023poster

Autonomous exploration in an unknown environment is widely studied, and many exploration strategies exist. However, in most of the works, safety is not usually given top priority. The reason behind the violation of safety by most of the exploration algorithms in real-world applications is the ignora…

Cited by 3 Source ↗Scholar ↗