Manav Kulshrestha — accepted AI-conference papers

2026

Scalable Multi-Robot Informative Path Planning for Target Mapping via Deep Reinforcement Learning

Apoorva Vashisth, Manav Kulshrestha, Damon Conover, Aniket Bera

RA-L 2026

Autonomous robots are widely utilized for mapping and exploration tasks due to their cost-effectiveness. Multi-robot systems offer scalability and efficiency, especially in terms of the number of robots deployed in more complex environments. These tasks belong to the set of Multi-Robot Informative P

Cited by 1 Source ↗code ↗Scholar ↗

2023

Structural Concept Learning via Graph Attention for Multi-Level Rearrangement Planning

Manav Kulshrestha, Ahmed H Qureshi

CoRL 2023poster

Robotic manipulation tasks, such as object rearrangement, play a crucial role in enabling robots to interact with complex and arbitrary environments. Existing work focuses primarily on single-level rearrangement planning and, even if multiple levels exist, dependency relations among substructures ar…

Cited by 6 Source ↗code ↗Scholar ↗