1 code implementation • 3 Apr 2024 • Cristian C. Beltran-Hernandez, Nicolas Erbetti, Masashi Hamaya
Our approach involves using Reinforcement Learning (RL) to train a robot to compliantly manipulate a knife, by reducing the contact forces exerted by the food items and by the cutting board.
no code implementations • 28 Feb 2024 • Hai Nguyen, Tadashi Kozuno, Cristian C. Beltran-Hernandez, Masashi Hamaya
This study tackles the representative yet challenging contact-rich peg-in-hole task of robotic assembly, using a soft wrist that can operate more safely and tolerate lower-frequency control signals than a rigid one.
1 code implementation • 2 Nov 2023 • Keisuke Shirai, Cristian C. Beltran-Hernandez, Masashi Hamaya, Atsushi Hashimoto, Shohei Tanaka, Kento Kawaharazuka, Kazutoshi Tanaka, Yoshitaka Ushiku, Shinsuke Mori
By generating PDs from language instruction and scene observation, we can drive symbolic planners in a language-guided framework.
no code implementations • NeurIPS 2023 • Yueh-Hua Wu, Xiaolong Wang, Masashi Hamaya
This paper introduces Elastic Decision Transformer (EDT), a significant advancement over the existing Decision Transformer (DT) and its variants.
1 code implementation • 18 Nov 2020 • Felix von Drigalski, Devwrat Joshi, Takayuki Murooka, Kazutoshi Tanaka, Masashi Hamaya, Yoshihisa Ijiri
In this paper, we present a diabolo model that can be used for training agents in simulation to play diabolo, as well as running it on a real dual robot arm system.
2 code implementations • 28 Sep 2019 • Mohammadamin Barekatain, Ryo Yonetani, Masashi Hamaya
Transfer reinforcement learning (RL) aims at improving the learning efficiency of an agent by exploiting knowledge from other source agents trained on relevant tasks.