Saumya Saxena
Papers
4
Total Citations
38
H-Index
4
About
Saumya Saxena’s research lies at the intersection of robotic manipulation, lifelong learning, and task planning—addressing fundamental challenges in making robots adaptable and robust in real-world environments. Her major contributions include pioneering active task-oriented exploration policies that bridge the sim-to-real gap, enabling robotic policies trained in simulation to transfer effectively to physical systems without relying on brittle domain randomization. She also advanced lifelong robotic manipulation by developing search-based task planning with learned skill effect models, allowing robots to acquire new skills and solve novel tasks over time without requiring rigid assumptions about skill structure. Her most-cited work, with 15 citations, demonstrates significant impact in the robotics community. Saxena’s research is notable for its practical focus on overcoming deployment barriers, such as dynamic mismatches between simulation and reality, and for enabling robots to autonomously expand their capabilities in open-ended settings. Her work is essential reading for researchers interested in scalable, generalizable robotic systems that can operate continuously in human environments.
Research Focus
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Top Papers
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