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Curriculum-based humanoid robot identification using large-scale human motion database

Frontiers in robotics and AI, 2023-11, Vol.10, p.1282299-1282299

2023

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MOSAIC for Multiple-Reward Environments

Neural computation, 2012-03, Vol.24 (3), p.577-606

2012

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Explicit contextual information selectively contributes to predictive switching of internal models

Experimental brain research, 2007-08, Vol.181 (3), p.395-408

2007

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Trial and Error: Using Previous Experiences as Simulation Models in Humanoid Motor Learning

IEEE robotics & automation magazine, 2016-03, Vol.23 (1), p.96-105

2016

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Brain-machine interfacing control of whole-body humanoid motion

Frontiers in systems neuroscience, 2014-08, Vol.8 (138), p.138-138

2014

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Hierarchical reinforcement learning for biped locomotion

Neuroscience research, 2009, Vol.65, p.S183-S183

2009

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Switching multiple LQG controllers based on Bellman's optimality: Using full-state feedback to control a humanoid robot

Neuroscience research, 2011-09, Vol.71, p.e409-e409

2011

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The eMOSAIC model for humanoid robot control

Neural networks, 2012-05, Vol.29-30, p.8-19

2012

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Reinforcement Learning for High-Dimensional Systems in Real Environments

Journal of The Society of Instrument and Control Engineers, 2013/08/10, Vol.52(8), pp.742-748

2013

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Switching multiple LQG controllers based on bellman's optimality principle: Using full-state feedback to control a humanoid robot

2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2011, p.3185-3191

2011

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Phase-dependent trajectory optimization for CPG-based biped walking using path integral reinforcement learning

2011 11th IEEE-RAS International Conference on Humanoid Robots, 2011, p.255-260

2011

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Motion capture and reinforcement learning of dynamically stable humanoid movement primitives

2013 IEEE International Conference on Robotics and Automation, 2013, p.5284-5290

2013

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eMOSAIC model for humanoid robot control

Neuroscience research, 2010, Vol.68, p.e329-e330

2010

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Trajectory-model-based reinforcement learning: Application to bimanual humanoid motor learning with a closed-chain constraint

2013 13th IEEE-RAS International Conference on Humanoid Robots (Humanoids), 2013, p.429-434

2013

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Reinforcement Learning with Reusing Mechanism of Avoidance Actions and its Application to Learning Whole-Body Motions of Multi-Link Robot

Journal of the Robotics Society of Japan, 2009, Vol.27(2), pp.209-220

2009

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《第9回》高次元・実環境における強化学習

計測と制御, 2013/08/10, Vol.52(8), pp.742-748

2013

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eMOSAIC Model for Humanoid Robot Control

From Animals to Animats 11, p.447-457

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Off-line path integral reinforcement learning using stochastic robot dynamics approximated by sparse pseudo-input Gaussian processes: Application to humanoid robot motor learning in the real environment

2013 IEEE International Conference on Robotics and Automation, 2013, p.1311-1316

2013

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BCI Control of Whole-Body Simulated Humanoid by Combining Motor Imagery Detection and Autonomous Motion Planning

Neural Information Processing, p.310-318

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Brain-controlled exoskeleton robot for BMI rehabilitation

2012 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2012), 2012, p.21-27

2012

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Towards Verification and Validation in Multiagent-Based Systems and Simulations: Analyzing Different Learning Bargaining Agents

Lecture notes in computer science, 2003, p.26-42

2003

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Cross-Element Validation in Multiagent-Based Simulation: Switching Learning Mechanisms in Agents

Journal of artificial societies and social simulation, 2003-10, Vol.6 (4)

2003

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回避行動の再利用メカニズムを備えた強化学習手法と多関節ロボットの全身運動学習への応用

日本ロボット学会誌, 2009, Vol.27(2), pp.209-220

2009

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Efficient reuse of previous experiences in humanoid motor learning

2014 IEEE-RAS International Conference on Humanoid Robots, 2014, p.554-559

2014

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Real-time Utilization of System-scale Neuroscience Models

Nihon Shinkei Kairo Gakkai shi, 2015, Vol.22 (3), p.125-132

2015

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Curriculum-based humanoid robot identification using large-scale human motion database

MOSAIC for Multiple-Reward Environments

Explicit contextual information selectively contributes to predictive switching of internal models

Trial and Error: Using Previous Experiences as Simulation Models in Humanoid Motor Learning

Brain-machine interfacing control of whole-body humanoid motion

Hierarchical reinforcement learning for biped locomotion

Switching multiple LQG controllers based on Bellman's optimality: Using full-state feedback to control a humanoid robot

The eMOSAIC model for humanoid robot control

Reinforcement Learning for High-Dimensional Systems in Real Environments

Switching multiple LQG controllers based on bellman's optimality principle: Using full-state feedback to control a humanoid robot

Phase-dependent trajectory optimization for CPG-based biped walking using path integral reinforcement learning

Motion capture and reinforcement learning of dynamically stable humanoid movement primitives

eMOSAIC model for humanoid robot control

Trajectory-model-based reinforcement learning: Application to bimanual humanoid motor learning with a closed-chain constraint

Reinforcement Learning with Reusing Mechanism of Avoidance Actions and its Application to Learning Whole-Body Motions of Multi-Link Robot

《第9回》高次元・実環境における強化学習

eMOSAIC Model for Humanoid Robot Control

Off-line path integral reinforcement learning using stochastic robot dynamics approximated by sparse pseudo-input Gaussian processes: Application to humanoid robot motor learning in the real environment

BCI Control of Whole-Body Simulated Humanoid by Combining Motor Imagery Detection and Autonomous Motion Planning

Brain-controlled exoskeleton robot for BMI rehabilitation

Towards Verification and Validation in Multiagent-Based Systems and Simulations: Analyzing Different Learning Bargaining Agents

Cross-Element Validation in Multiagent-Based Simulation: Switching Learning Mechanisms in Agents

回避行動の再利用メカニズムを備えた強化学習手法と多関節ロボットの全身運動学習への応用

Efficient reuse of previous experiences in humanoid motor learning

Real-time Utilization of System-scale Neuroscience Models

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