Papers
10
Total Citations
176
H-Index
7
About
Seyedshams Feyzabadi is a leading researcher at the intersection of robotics, autonomous decision-making, and surgical automation. His work primarily focuses on risk-aware path planning and hierarchical constrained Markov Decision Processes (CMDPs), where he has pioneered methods for mobile robots—such as autonomous forklifts—to navigate safely alongside humans in industrial environments. His foundational paper on risk-aware path planning (2014, 55 citations) introduced a framework that balances efficiency with risk mitigation, a critical contribution for next-generation human-robot collaboration. In the medical domain, Feyzabadi has made significant strides in robot-assisted surgery (RAS). He developed daVinciNet (2020, 26 citations), a deep learning model that jointly predicts surgical instrument trajectories and subtask states, enabling shared control and supervision. His work on temporal segmentation of surgical subtasks (2020, 38 citations) using multiple data sources has advanced the automation of complex surgical procedures. With over 150 total citations across his publications, Feyzabadi’s research bridges theoretical planning algorithms and practical deployment in both industrial and clinical settings, establishing him as a key figure in safe, intelligent robotic systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4Multi-objective planning with multiple high level task specifications15 citations · 2016
- 5SLAM à la carte - GPGPU for Globally Consistent Scan Matching.10 citations · 2011
- 6Planning using hierarchical constrained Markov decision processes10 citations · 2017
- 7
- 8HCMDP: A hierarchical solution to Constrained Markov Decision Processes7 citations · 2015
- 9
- 10