3 papers
cs.RO2025
TACT: Humanoid Whole-body Contact Manipulation through Deep Imitation Learning with Tactile Modality
Masaki Murooka, Takahiro Hoshi, Kensuke Fukumitsu +5
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…
cs.RO2025
Whole-body Multi-contact Motion Control for Humanoid Robots Based on Distributed Tactile Sensors
Masaki Murooka, Kensuke Fukumitsu, Marwan Hamze +4
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 intermedi…
cs.RO2024
Learning Bipedal Walking for Humanoid Robots in Challenging Environments with Obstacle Avoidance
Marwan Hamze, Mitsuharu Morisawa, Eiichi Yoshida
Deep reinforcement learning has seen successful implementations on humanoid robots to achieve dynamic walking. However, these implementations have been so far successful in simple…