Nestor Arana-Arexolaleiba
Papers
9
Total Citations
216
H-Index
6
About
Nestor Arana-Arexolaleiba is a robotics researcher whose work sits at the intersection of reinforcement learning, collaborative robotics, and intelligent manufacturing. His research focuses primarily on applying machine learning — particularly reinforcement learning — to contact-rich manipulation and disassembly tasks, areas critical to advancing flexible, adaptive industrial automation in the Industry 4.0 era. His most influential contribution, a comprehensive review on reinforcement learning for contact-rich robotic manipulation (2022), has garnered 141 citations, establishing him as a key voice in this rapidly growing field. Beyond theoretical grounding, Arana-Arexolaleiba has consistently pursued practical applications, including goal-conditioned reinforcement learning within human-robot disassembly environments and transferring human manipulation knowledge to industrial robots — work that directly addresses real-world flexibility challenges in remanufacturing. A distinguishing thread in his research is human-robot interaction (HRI), where he investigates how interface design, robot kinematics, and multimodal communication affect worker well-being, cognitive workload, and safety. His more recent studies leverage EEG-based measurement to objectively evaluate operator experience, reflecting a rigorous, human-centered approach. Through frameworks supporting KUKA LBR iiwa robots and explorations into visual odometry, Arana-Arexolaleiba demonstrates a broad yet purposeful technical range aimed at making intelligent robotics genuinely deployable in collaborative industrial settings.
Research Focus
Key Achievements
Top Papers
- 1A review on reinforcement learning for contact-rich robotic manipulation tasks141 citations · 2022
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- 9Image Enhancement using GANs for Monocular Visual Odometry3 citations · 2021