Pranay Thangeda
Papers
2
Total Citations
11
H-Index
2
About
Pranay Thangeda is a robotics researcher whose work focuses on enabling autonomous systems to operate robustly in unknown and unpredictable environments. His key research areas include robotic manipulation, adaptive sampling, and machine learning for domain adaptation, with a particular emphasis on extraterrestrial exploration and planetary science. Thangeda’s major contributions center on developing algorithms that allow robots to adapt to novel conditions without extensive retraining. His most cited work, "Few-shot Adaptation for Manipulating Granular Materials Under Domain Shift" (2023, 7 citations), introduces a deep Gaussian process method trained with meta-learning to enable autonomous lander missions to sample granular materials on extraterrestrial bodies, even when Earth-tuned strategies fail. This work addresses a critical challenge for future space exploration missions. In "Adaptive Sampling Site Selection for Robotic Exploration in Unknown Environments" (2022, 4 citations), he tackles the problem of autonomously selecting optimal sampling locations under constraints and risk of system failure, a key capability for long-duration exploration missions. Thangeda’s research is notable for bridging the gap between simulation-trained policies and real-world deployment, directly impacting the design of autonomous systems for planetary science. His work on domain shift and adaptive sampling is foundational for next-generation robotic explorers.
Research Focus
Key Achievements
Top Papers
- 1
- 2