Research

Project

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  Inclusive robotic foundation model (JST CRONOS)
  We develop a world model connected to a foundation model that can optimize the actions for various robots in response to language instrutions.
  • Mapping between latent action space common among robots and robot-specific action spaces
  • Lightweight hypernetworks that switch internal state representation according to language instrutions
  • Learning world model interpreted as multi-objective optimization
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  Skill-transfer AI model (JST K Program)
  We develop three AI models that extract the skill differences between novice and expert learners and efficiently transfer the necessary skills to novice learners.
  • Extraction of skill differences by skillful AI model
  • Interface optimization for skill transfer through instructional AI model
  • Interface selection/balance based on biometrics/preference using personal AI model

Reinforcement learning

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  Reinforcement learning as probabilistic inference
  We derive and analyze various values that can be found by interpreting RL as a kind of probabilistic inference problem.
  • General representation of optimality and divergence
  • Optimization of discount factor according to events
  • Weber-Fechner law in TD learning
  • Distributional model with regular optimism and pessimism
  • Theoretically-grounded optimistic learning
  • Integration with feedback error learning
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  Model-based reinforcement learning
  We study learning theory of world model and its application to model predictive control in real time.
  • Generation and validity-based optimal use of pseudo-experiential data
  • Adversarial learning with moderate robustness and less conservativeness
  • Model predictive control with efficient convergence to a suboptimal solution
  • Extraction of sparse low-dimensional latent space
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  Stabilization of deep reinforcement learning
  We develop stabilization techniques that enable deep reinforcement learning to optimize policies stably.
  • Robustness to uncertainty in reward and value estimation
  • Analysis of experience replayable conditions
  • Stable and fast target network updates
  • Regularization of local Lipschitz continuity of policy and value functions

Imitation learning

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  Utilization of imperfect demonstration
  We develop imitation learning methodologies in the absence of sufficient quality, modality, and quantity of demonstration data.
  • Spatiotemporal partial imitation through self-paced learning
  • Robust behavioral cloning for outliers based on Tsallis statistics
  • Safe and efficient framework to compensate for missing action data
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  Interactive imitation learning
  We study a framework in which the demonstrator and agent collaborate to collect data to be imitated.
  • Active exploration without sacrificing the sense of agency
  • Optimal consensus decision-making among multiple actions with confidences

Other machine leanring

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  Time-series data processing
  We design models to process time-series data, which is important for analyzing and representing robot and human motions.
  • Model structure for both long-term memory and stability
  • Learning theory of recurrent neural networks based on variational inference
  • Design of reservoir computing in complex domain that embeds the representation of periodicity
  • Approximated implementation of neuron dynamics following a power law
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  Stochastic gradient descent
  We improve the performance of the stochastic gradient descent, which is a core technique in deep learning.
  • Novel interpretation of dual structured algorithm
  • Robustness to noise and outliers based on Student's t-distribution
  • Improvement of AMSGrad to make it applicable to non-stationary problems
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  Lifelong (continual) learning
  We develop fundamental technologies for autonomous robots to keep learning continuously.
  • Crossmodal learning to complement missing modalities
  • Balancing memory stability and plasticity
  • Fractal model for continual learning of multiple tasks

Domain-oriented

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  Humanoid robots
  We study motion control of humanoid robots, which are expected to play an active role in human society as general-purpose robots.
  • Sim2real reinforcement learning for redundant robots
  • Integration of whole-body model predictive control and machine learning
  • Dancing with a partner using whole-body skin sensors
  • Unified controller for bipedal walking and running and continuous transition between them
• 
  Human assistive robot
  We develop AI technologies to assist various people.
  • Semi-automation towards safe teleoperation systems
  • Extracting and understanding the tacit knowledge hidden in subjective evaluation
  • Surgical assistance with learning-based model predictive control
  • Learning assistance in embryo manipulation with extracted expert skills
  • Locomotion assistance based on recognition of human movement behaviors