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.
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.
Robust Control / Adaptive control
We study advanced theories in post-modern robust/adaptive control and their applications. Various schemes of feedforward learning control(feedback error learning) are currently under investigation.
Independent Component Analysis
In this research, we study system identification, fault detection, and disturbance rejection via independent component analysis.
Probabilistic inference based optimal control / Reinforcement learning
We study stochastic optimal control and reinforcement learning.
Motor Skill Learning for Humanoid Robots
We are developing novel methods that enable robots to learn complex motor skills (e.g., biped walking, T-shirt wearing and clothing assistance) by optimal control and reinforcement learning.
Full-body Exoskeleton Robot Control for Walking Assistance
We propose an adaptive walking assistance strategy to control a full-body exoskeleton robot using an oscillator model.
(joint work with ATR CNS)
Active Motion Planning of Robots Based on Information Theoretic Criteria
We propose an active motion planning principle and its calculation algorithm based on information theoretic criteria such as the mutual information.
Intrinsically Motivation for Autonomous Robots
In order for the robot to truly autonomously work, it is necessary for the robot to determine the purpose by themselves rather than getting the purpose from designers. We are studying autonomous behavior acquisition of robot by designing reward based on the concept of intrinsic motivation.
Reservoir Computing for Multi-Objective Switchable Control
For the robot to acquire complex nonlinear dynamics such as human motion, it is effective to have nonlinear dynamics in the controller. We are studying motion control technology utilizing nonlinear dynamics of reservoir computing.
Marketings in Large-scale Online Social Networks
We are developing computationally efficient algorithms for locating the seeding nodes having the maximum influence on the outbreak of spreading processes over complex networks, toward achieving effective viral marketings in large-scale online social networks.
We are developing large-scale algorithms for optimally tuning the network centralities (e.g., the PageRank) in complex networks under budget constraints.
Effectiveness and Optimality of the Bet-hedging Strategy
We are investigating the effectiveness and optimality of the bet-hedging strategy, an intrinsic mechanism of micro bio-populations for surviving through environmental fluctuations, by utilizing the analytical tools from the systems and control theory.
System Identification with Manifold Learning
We propose a nonlinear system identification scheme with input-output manifold learning. The scheme is based on a nonlinear dimensionality reduction method regularized by the latent dynamics structure.