Nobutomo Matsunaga

About

Nobutomo Matsunaga is a robotics researcher whose work spans human-robot interaction safety, cooperative multi-robot systems, and bio-inspired sensing. His research is united by a fundamental concern: how can robots operate safely and effectively alongside humans in shared environments? Matsunaga's most distinctive contribution lies in his pioneering development of computational pain perception models for robots. Recognizing that traditional safety barriers are impractical in human-robot coexistence scenarios, he pursued a novel paradigm — equipping robots with artificial pain sensing modeled on human skin physiology. Using artificial neural networks and layered sensor architectures, his team emulated both superficial and deep pain responses, including the neurologically distinct fast and slow pain phenomena, generating a body of work accumulating over 40 citations across multiple publications. In parallel, Matsunaga has made significant advances in cooperative multi-robot transportation, employing cutting-edge reinforcement learning frameworks including MADDPG and Deep Dyna-Q to improve formation control and learning efficiency in multi-agent systems. His work on pneumatic artificial muscle actuators further reflects his commitment to compliant, human-safe robotic motion. Collectively, Matsunaga's research bridges biomechanics, machine learning, and robotics control, offering practical pathways toward robots that are not merely capable, but genuinely safe partners in human environments.

Research Focus

Key Achievements

Top Papers

1. 1
   Modeling of Superficial Pain using ANNs

   13 citations · 2005
2. 2
   Formation path learning for cooperative transportation of multiple robots using MADDPG

   12 citations · 2021
3. 3
   Deep Dyna-Q for Rapid Learning and Improved Formation Achievement in Cooperative Transportation

   10 citations · 2023
4. 4
   Robust variable stiffness control of McKibben type pneumatic artificial muscle arm by using multiple model error compensators

   10 citations · 2017
5. 5
   Pain perception model of human skin using multiple pain sensors

   8 citations · 2010
6. 6
   Kinematic analysis of human lifting movement for biped robot control

   7 citations · 2004
7. 7
   Superficial pain model using ANNs and its application to robot control

   7 citations · 2006
8. 8
   Emulation of Fast and Slow Pains Using Multi-Layer Sensor Modeled the Layered Structure of Human Skin

   6 citations · 2011
9. 9
   Robust Cooperative Transport System with Model Error Compensator Using Multiple Robots with Suction Cups

   6 citations · 2023
10. 10
    Experimental Analysis and Modeling of Superficial Pain on Upper Limb

    6 citations · 2006

Key Collaborators

Contact & Links