Papers
11
Total Citations
71
H-Index
5
About
Keiki Takadama is a pioneering researcher at the intersection of space robotics, swarm intelligence, and reinforcement learning. His work fundamentally explores how multiple autonomous systems—whether robots in space or agents in software—can learn to coordinate, adapt, and recover from failures without central control. Takadama’s most influential contribution is the concept of inflatable tensegrity for large space structures (16 citations), proposing a revolutionary approach where robotic manipulators automatically assemble and maintain vast solar power satellites using inflatable compression elements. He has also made significant strides in swarm robotics, developing learning models that enable organized groups of robots to exhibit adaptive collective behaviors through local interactions (14 citations). His research on fail-safe design for multiple space robots introduced novel reinforcement learning methods that allow robots to complete tasks even when communication fails (5 citations). More recently, Takadama has explored guiding robot exploration by integrating automated planning with reinforcement learning (8 citations), and has investigated human continuous learning ability through the lens of reflection cost (6 citations). His work on wireless mesh network deployment using RSSI-based swarm robots (9 citations) demonstrates practical applications of his theoretical frameworks.
Research Focus
Key Achievements
Top Papers
- 1Concept of Inflatable Tensegrity for Large Space Structures16 citations · 2006
- 2Learning model for adaptive behaviors as an organized group of swarm robots14 citations · 1998
- 3Deployment of wireless mesh network using RSSI-based swarm robots9 citations · 2016
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- 5Analyzing human's continuous learning ability with the reflection cost6 citations · 2015
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