Naoto Hoshikawa
Papers
2
Total Citations
8
H-Index
2
About
Naoto Hoshikawa’s research lies at the intersection of robotics, artificial intelligence, and embodied cognition, with a particular focus on tactile sensing and autonomous behavior acquisition. His most cited work, “Two Methodologies Toward Artificial Tactile Affordance System in Robotics” (2010, 5 citations), introduces a groundbreaking concept: applying Gibson’s theory of affordance to robotic systems. By emphasizing tactile sensing over traditional visual or planning-based approaches, Hoshikawa proposes that robots can bypass complex recognition and planning stages, enabling more direct and adaptive interaction with their environment. This work challenges conventional top-down AI paradigms and offers a pathway toward more intuitive, sensor-driven robotic behavior. In his second notable paper, “Bottom-Up Approach for Behavior Acquisition of Agents Equipped With Multi-Sensors” (2011, 3 citations), Hoshikawa addresses the frame problem in AI by advocating for evolutionary, bottom-up methodologies. He introduces the Evolutionary Behavior Table System (EBTS), a framework that allows agents to autonomously develop behaviors through multi-sensor integration and simulated evolution. Though his citation counts are modest, Hoshikawa’s contributions are conceptually rich, offering foundational ideas for researchers exploring tactile affordance, sensorimotor learning, and bio-inspired robotics. His work is particularly valuable for those seeking alternatives to computationally intensive top-down approaches in autonomous systems.
Research Focus
Key Achievements
Top Papers
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