Steve Susanibar
Papers
1
Total Citations
9
H-Index
1
About
Steve Susanibar’s research lies at the intersection of robotic manipulation, soft object handling, and contact-rich control, with a focus on enabling robots to operate seamlessly in human environments. His most-cited work, “Polymorphic robot learning for dynamic and contact-rich handling of soft-rigid objects” (2017, 9 citations), addresses a critical challenge in robotics: transitioning from rigid object grasping to adaptive handling of deformable and soft-rigid objects. By developing polymorphic learning strategies, Susanibar has contributed to making robots more versatile in dynamic, real-world interactions, such as managing slip and contact without compromising task success. This work is foundational for applications in domestic assistance, healthcare, and manufacturing, where robots must adapt to unpredictable object behaviors. While his citation count reflects a focused, emerging impact, his research is notable for bridging the gap between rigid and soft object manipulation—a key frontier in modern robotics. Susanibar’s contributions are particularly valuable for students and researchers exploring adaptive control and learning in unstructured environments, offering a pathway toward more intuitive and capable robotic systems.
Research Focus
Key Achievements
Top Papers
- 1