Papers
12
Total Citations
493
H-Index
7
About
Gautam Salhotra is a leading researcher in robotics, with key contributions spanning robotic autonomy, manipulation, and large-scale learning. He is best known for his pivotal role in the DARPA Subterranean Challenge as part of Team CoSTAR, where he helped develop the NeBula autonomy solution—a sophisticated system of algorithms, hardware, and software that enabled robots to navigate and explore challenging, unknown environments. This work, published in a series of highly cited papers (over 100 citations each), demonstrated groundbreaking advances in autonomous exploration under uncertainty. Salhotra also made significant contributions to the Open X-Embodiment project, a collaborative effort that produced large, general-purpose robotic learning datasets and models (RT-X), amassing over 200 citations. His research further extends to deformable object manipulation, where he introduced a novel Learning from Demonstration method (DMfD), and to reinforcement learning for manipulation in obstructed environments. With a publication record that includes papers on hierarchical value learning (PLGRIM) and adaptive sampling using POMDPs, Salhotra’s work is characterized by its breadth and impact, advancing both the theoretical foundations and practical capabilities of autonomous robots.
Research Focus
Key Achievements
Top Papers
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- 3Open X-Embodiment: Robotic Learning Datasets and RT-X Models101 citations · 2023
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- 6Learning Deformable Object Manipulation From Expert Demonstrations32 citations · 2022
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- 10Adaptive Sampling using POMDPs with Domain-Specific Considerations4 citations · 2021