Kensuke Fukumitsu — accepted AI-conference papers

2025

TACT: Humanoid Whole-Body Contact Manipulation Through Deep Imitation Learning With Tactile Modality

Masaki Murooka, Takahiro Hoshi, Kensuke Fukumitsu, Shimpei Masuda, Marwan Hamze, Tomoya Sasaki +2

RA-L 2025

Manipulation with whole-body contact by humanoid robots offers distinct advantages, including enhanced stability and reduced load. On the other hand, we need to address challenges such as the increased computational cost of motion generation and the difficulty of measuring broad-area contact. We the

Cited by 8 Source ↗Scholar ↗

2024

Whole-Body Multi-Contact Motion Control for Humanoid Robots Based on Distributed Tactile Sensors

Masaki Murooka, Kensuke Fukumitsu, Marwan Hamze, Mitsuharu Morisawa, Hiroshi Kaminaga, Fumio Kanehiro +1

RA-L 2024

To enable humanoid robots to work robustly in confined environments, multi-contact motion that makes contacts not only at extremities, such as hands and feet, but also at intermediate areas of the limbs, such as knees and elbows, is essential. We develop a method to realize such whole-body multi-con

Cited by 10 Source ↗code ↗Scholar ↗