5 citations · 8 across the 5 of their papers we have counts for
4 papers · 1 filter
Don't Risk It!: Trajectory-Conditioned Distributional Risk for LiDAR-Only Crowd Navigation
Mathai Mathew Pulicken, Laksh Nanwani, Basant Sharma +3
Efficient crowd navigation requires anticipating dynamic collision-free space without relying on fragile object tracking. We propose the Don't Risk It Planner (DRIP), an end-to-end…
Crowd-FM: Learned Optimal Selection of Conditional Flow Matching-generated Trajectories for Crowd Navigation
Antareep Singha, Laksh Nanwani, Mathai Mathew P. +4
Safe and computationally efficient local planning for mobile robots in dense, unstructured human crowds remains a fundamental challenge. Moreover, ensuring that robot trajectories…
CrowdSurfer: Sampling Optimization Augmented with Vector-Quantized Variational AutoEncoder for Dense Crowd Navigation
Naman Kumar, Antareep Singha, Laksh Nanwani +6
Navigation amongst densely packed crowds remains a challenge for mobile robots. The complexity increases further if the environment layout changes, making the prior computed global…
Instance-Level Semantic Maps for Vision Language Navigation
Laksh Nanwani, Anmol Agarwal, Kanishk Jain +7
Humans have a natural ability to perform semantic associations with the surrounding objects in the environment. This allows them to create a mental map of the environment, allowing…