18 citations · 28 across the 18 of their papers we have counts for
21 papers
Learning Locally, Communicating Globally: Reinforcement Learning of Multi-robot Task Allocation for Cooperative Transport
Kazuki Shibata, Tomohiko Jimbo, Tadashi Odashima +2
We consider task allocation for multi-object transport using a multi-robot system, in which each robot selects one object among multiple objects with different and unknown weights.…
Deep reinforcement learning of event-triggered communication and consensus-based control for distributed cooperative transport
Kazuki Shibata, Tomohiko Jimbo, Takamitsu Matsubara
In this paper, we present a solution to a design problem of control strategies for multi-agent cooperative transport. Although existing learning-based methods assume that the numbe…
Bayesian Disturbance Injection: Robust Imitation Learning of Flexible Policies for Robot Manipulation
Hanbit Oh, Hikaru Sasaki, Brendan Michael +1
Humans demonstrate a variety of interesting behavioral characteristics when performing tasks, such as selecting between seemingly equivalent optimal actions, performing recovery ac…
Deep Koopman with Control: Spectral Analysis of Soft Robot Dynamics
Naoto Komeno, Brendan Michael, Katharina Küchler +2
Soft robots are challenging to model and control as inherent non-linearities (e.g., elasticity and deformation), often requires complex explicit physics-based analytical modeling (…
Physically Consistent Preferential Bayesian Optimization for Food Arrangement
Yuhwan Kwon, Yoshihisa Tsurumine, Takeshi Shimmura +2
This paper considers the problem of estimating a preferred food arrangement for users from interactive pairwise comparisons using Computer Graphics (CG)-based dish images. As a foo…
Goal-Aware Generative Adversarial Imitation Learning from Imperfect Demonstration for Robotic Cloth Manipulation
Yoshihisa Tsurumine, Takamitsu Matsubara
Generative Adversarial Imitation Learning (GAIL) can learn policies without explicitly defining the reward function from demonstrations. GAIL has the potential to learn policies wi…