Ibai Inziarte-Hidalgo
Papers
7
Total Citations
175
H-Index
3
About
Ibai Inziarte-Hidalgo is a rising researcher at the intersection of reinforcement learning (RL) and robotics, with a focus on contact-rich manipulation, motion planning, and human-robot collaboration. His most cited work, a comprehensive review on RL for contact-rich robotic manipulation (141 citations), has become a key reference for researchers tackling the challenges of unstructured environments and hard-to-engineer behaviors. Inziarte-Hidalgo has also made notable contributions to goal-conditioned RL in human-robot disassembly environments, advancing safer and more efficient industrial workflows. His research extends into high-stakes medical applications, including RL-based control for collaborative robotic brain retraction and robotic-arm force control in neurosurgery—areas where precision and safety are paramount. Additionally, he has developed a novel automated interactive RL framework with a constraint-based supervisor for procedural tasks, addressing the limitations of reward function design. With over 175 total citations and a growing portfolio of work that bridges theoretical RL with real-world robotic systems, Inziarte-Hidalgo is establishing himself as a thoughtful contributor to both industrial and medical robotics.
Research Focus
Key Achievements
Top Papers
- 1A review on reinforcement learning for contact-rich robotic manipulation tasks141 citations · 2022
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- 4Robotic-Arm-Based Force Control in Neurosurgical Practice3 citations · 2023
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