Alvika Gautam
Papers
8
Total Citations
98
H-Index
5
About
Alvika Gautam is pioneering the frontier of trustworthy autonomy, focusing on how robots can understand and communicate their own limitations. Her core research lies at the intersection of human-robot interaction (HRI) and self-assessment, where she develops frameworks for robots to evaluate their proficiency in real-time. Her most influential work introduces the concept of "Assumption-Alignment Tracking" (AAT), a method that allows robots to monitor their own performance against design assumptions, enabling them to "know their limits" without requiring risky, real-world testing. This work, published in top venues and accumulating over 80 citations, directly addresses a critical barrier to deploying autonomous systems in safety-critical domains. Gautam has also made significant contributions to explainable robotics, comparing reactive versus proactive failure explanations to build human trust. Her studies on cooperation, balancing efficiency and risk in human-robot teams, further underscore her holistic approach to designing machines that are not only capable but also transparent and safe partners. By giving robots the ability to self-diagnose and explain their failures, Gautam is laying the essential groundwork for a future where we can confidently collaborate with autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2Robot Proficiency Self-Assessment Using Assumption-Alignment Tracking19 citations · 2023
- 3A Method for Designing Autonomous Robots that Know Their Limits19 citations · 2022
- 4Reactive or Proactive? How Robots Should Explain Failures17 citations · 2024
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