Aastha Acharya
University of Colorado Boulder, University of Colorado System
Papers
2
Total Citations
12
H-Index
2
About
Aastha Acharya is a researcher at the forefront of human-robot interaction and autonomous systems, with a focus on building trust and efficiency in robotic decision-making. Her work centers on two critical challenges: enabling autonomous vehicles to accurately self-assess their competency, and teaching robots to learn from human preferences for more effective exploration. In her most cited work (2022, 9 citations), Acharya introduced a novel deep reinforcement learning framework that allows autonomous systems to generalize their competency self-assessment across diverse scenarios, directly addressing the human trust calibration problem—a key barrier to deploying robots alongside people. Her earlier research (2020, 3 citations) pioneered an iterative reward learning approach for robotic exploration, aiming to reduce the need for constant human supervision in planetary missions by allowing robots to adapt their behavior based on operator feedback. This work tackles the fundamental challenge of communication delays in space exploration. Acharya’s contributions are particularly notable for bridging the gap between theoretical reinforcement learning and practical, safety-critical applications, making her a rising voice in the quest for more autonomous and trustworthy robotic systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2Iterative Reward Learning for Robotic Exploration3 citations · 2020