Research Topics

Now that ubiquitous society has come, computerized/intelligent control systems are achievable everywhere. Our laboratory is working on system control, machine learning, signal processing, and interdisciplinary study based on these topics. In what follows, after stating general overview, we show our current research topics. Please note that these are just examples and we always challenge a new topic.

— Intelligent System Control: What is our target —

As the term “control” is used in our daily life, this concept is familiar and basic. Control technology is widely used in industry, but at this moment only simple control laws are adopted in practice and higher performance is strongly desired. This can be achieved by modeling dynamics of the object and analyzing/designing the system in an intelligent way. It is also desirable to build a learning mechanism as a human shows improvement with his/her progress. It is thus our target to construct a highly intelligent control system. Because of its high level of abstraction, you might feel that it is not practical right now, but the knowledge you learn here will be surely useful when you enter the real world.

Summaries of topics conducted by students are here!

Ongoing research topics (examples)

Robust/Adaptive learning control

We are studying advanced control technology such as robust control and its application. For example we are currently studying Feedback Error Learning (FEL) a famous biological model of motion learning, from a control-theoretic viewpoint. We are also interested in data-driven control systems by making full use of computers.

Feedback Error Learning

Networked control/Switched system

We consider how control technology should be, in this era of Iot where everything is connected to the Internet. Specifically we are proposing a switched control system that is robust against random delay of signal transmission (jitter) or apriodic sampling (packet loss), and verifying their effectiveness via experiment with small vehicles such as drone.

Control system robust against signal loss

Neural network

We are developing new neural networks, e.g., deep learning and reservoir computing, to acquire the excellent performance of our brain.
(Click to see examples of this topic)

Neural network

Biologically-inspired learning

We are studying new (reinforcement) learning structures inspired by animals to be converted from mathematically convenient ones.
(Click to see examples of this topic)

Biologically-inspired learning

Human-robot interaction

We are developing technologies for next-generation robots that can physically interact with human and aim to support various human motions.
(Click to see examples of this topic)

Human-robot interaction

Multi-agent reinforcement learning

We are studying practical reinforcement learning algorithms for large-scale decentralized autonomous robot systems constructed in the future age with robots and AI.
(Click to see examples of this topic)

Multi-agent reinforcement learning

Research Equipments

DesktopPCs
Quanser 2DoFArm
Quanser ecp2055d
IP1
LEGO
ArDrone
Pololu
Dobot
QbRobot
Doggy
PhantomX
Minitaur
WheelChair