Takeshi Takahashi
University of Massachusetts Amherst, Carnegie Mellon University, Amherst College
Papers
8
Total Citations
112
H-Index
5
About
Takeshi Takahashi is a leading researcher in autonomous robotics, specializing in path planning, human-robot interaction, and belief-space planning for emergency response and rehabilitation. His most influential work, "Learning Heuristic Functions for Mobile Robot Path Planning Using Deep Neural Networks" (30 citations), revolutionized traditional algorithms like A* and D* by using deep learning to approximate true path costs, dramatically improving computational efficiency for mobile robots. Takahashi also made foundational contributions to search-and-rescue robotics, introducing continual planning frameworks that enable autonomous operation in perilous environments, as detailed in his 2015 paper (18 citations). His interdisciplinary impact extends to healthcare, where his 2013 study on humanoid-mediated teletherapy for stroke rehabilitation (18 citations) proposed extended virtual presence of therapists, blending robotics with clinical care. Additionally, his work on reconfigurable task representations in belief-space planning (2016) and efficient object recognition using pre-trained CNNs (2015) advanced robot adaptability under uncertainty. With over 110 total citations across his top papers, Takahashi’s research has shaped modern autonomous systems, from efficient navigation to life-saving emergency response, making him a pivotal figure in robotics.
Research Focus
Key Achievements
Top Papers
- 1
- 2An efficient system for combined route traversal and collision avoidance25 citations · 2008
- 3Towards extended virtual presence of the therapist in stroke rehabilitation18 citations · 2013
- 4Continual planning for search and rescue robots18 citations · 2015
- 5Reconfigurable tasks in belief-space planning8 citations · 2016
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- 8