Papers
12
Total Citations
261
H-Index
6
About
Samuel Schmidgall is a pioneering researcher at the intersection of artificial intelligence, robotics, and autonomous surgery, with a focus on brain-inspired learning and surgical autonomy. His major contributions include developing the Surgical Robot Transformer (SRT) and its hierarchical extension, SRT-H, which enable robots to learn complex surgical tasks through imitation learning and language-conditioned policies, addressing challenges like dexterous manipulation and tissue variability. He also created Surgical Gym, a high-performance GPU-based platform for reinforcement learning in surgical robotics, and explored tumor tracking under deformation using occupancy networks. His work has garnered over 250 citations, with his 2024 review on brain-inspired learning in ANNs alone accumulating 108 citations. Schmidgall’s notable achievements include advancing the vision of autonomous surgery, as highlighted in his review "Will your next surgeon be a robot?" which examines pathways to overcome human limitations in surgical outcomes. His research bridges neuroscience and robotics, offering transformative insights for students and researchers in AI, healthcare, and autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1Brain-inspired learning in artificial neural networks: A review108 citations · 2024
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- 5Will your next surgeon be a robot? Autonomy and AI in robotic surgery19 citations · 2025
- 6Surgical Robot Transformer (SRT): Imitation Learning for Surgical Tasks6 citations · 2024
- 7Brain-inspired learning in artificial neural networks: a review5 citations · 2023
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- 9Optimal Localized Trajectory Planning of Multiple Non-holonomic Vehicles3 citations · 2021
- 10Robots learning to imitate surgeons — challenges and possibilities3 citations · 2024