50 citations · 50 across the 4 of their papers we have counts for
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PACT: Posture-Aligned Co-Design Technique for Hand Configuration and Retargeting in Multi-Finger Imitation Learning
Koyo Sasazaki, Satoshi Funabashi, Pranav Ponnivalavan +3
The fidelity of teleoperated demonstrations used for imitation learning is determined by how faithfully a robot hand can replicate the operator's posture. The posture retargeting l…
TaSA: Two-Phased Deep Predictive Learning of Tactile Sensory Attenuation for Improving In-Grasp Manipulation
Pranav Ponnivalavan, Satoshi Funabashi, Alexander Schmitz +2
Humans can achieve diverse in-hand manipulations, such as object pinching and tool use, which often involve simultaneous contact between the object and multiple fingers. This is st…
Focused Blind Switching Manipulation Based on Constrained and Regional Touch States of Multi-Fingered Hand Using Deep Learning
Satoshi Funabashi, Atsumu Hiramoto, Naoya Chiba +3
To achieve a desired grasping posture (including object position and orientation), multi-finger motions need to be conducted according to the the current touch state. Specifically,…
FingerTac -- An Interchangeable and Wearable Tactile Sensor for the Fingertips of Human and Robot Hands
Prathamesh Sathe, Alexander Schmitz, Satoshi Funabashi +3
Skill transfer from humans to robots is challenging. Presently, many researchers focus on capturing only position or joint angle data from humans to teach the robots. Even though t…
Multi-Fingered In-Hand Manipulation with Various Object Properties Using Graph Convolutional Networks and Distributed Tactile Sensors
Satoshi Funabashi, Tomoki Isobe, Fei Hongyi +4
Multi-fingered hands could be used to achieve many dexterous manipulation tasks, similarly to humans, and tactile sensing could enhance the manipulation stability for a variety of…