18 citations · 21 across the 4 of their papers we have counts for
5 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…
Development of global optimal coverage control using multiple aerial robots
Kazuki Shibata, Tatsuya Miyano, Tomohiko Jimbo
Coverage control has been widely used for constructing mobile sensor network such as for environmental monitoring, and one of the most commonly used methods is the Lloyd algorithm…
Robust shape estimation with false-positive contact detection
Kazuki Shibata, Tatsuya Miyano, Tomohiko Jimbo +1
We propose a means of omni-directional contact detection using accelerometers instead of tactile sensors for object shape estimation using touch. Unlike tactile sensors, our contac…
Deep reinforcement learning of event-triggered communication and control for multi-agent cooperative transport
Kazuki Shibata, Tomohiko Jimbo, Takamitsu Matsubara
In this paper, we explore a multi-agent reinforcement learning approach to address the design problem of communication and control strategies for multi-agent cooperative transport.…