Alvika Gautam — accepted AI-conference papers

2024

Exploring Unstructured Environments Using Minimal Sensing on Cooperative Nano-Drones

Pedro Arias-Perez, Alvika Gautam, Miguel Fernández-Cortizas, David Pérez-Saura, Srikanth Saripalli, Pascual Campoy

RA-L 2024

Recent advances have improved autonomous navigation and mapping under payload constraints, but current multi-robot inspection algorithms are unsuitable for nano-drones, due to their need for heavy sensors and high computational resources. To address these challenges, we introduce <italic xmlns:mml="

Cited by 2 Source ↗code ↗Scholar ↗

2023

Proficiency Self-Assessment without Breaking the Robot: Anomaly Detection using Assumption-Alignment Tracking from Safe Experiments

Xuan Cao, Jacob W. Crandall, Ethan Pedersen, Alvika Gautam, Michael A. Goodrich

ICRA 2023poster

Proficiency self-assessment (PSA), the ability to assess how well one can carry out a task, is a desirable capability of autonomous robot systems. Prior work has proposed assumption-alignment tracking (AAT) for performing PSA, and has shown that it can accurately predict robot performance in real-ti…

Cited by 5 Source ↗Scholar ↗

2022

A Method for Designing Autonomous Robots that Know Their Limits

Alvika Gautam, Tim Whiting, Xuan Cao, Michael A. Goodrich, Jacob W. Crandall

ICRA 2022poster

While the design of autonomous robots often emphasizes developing proficient robots, another important attribute of autonomous robot systems is their ability to evaluate their own proficiency and limitations. A robot should be able to assess how well it can perform a task before, during, and after i…

Cited by 22 Source ↗Scholar ↗