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ORCIDTaisuke Kobayashi
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2020 – today
- 2024
[j28]Taisuke Kobayashi
:
Revisiting experience replayable conditions. Appl. Intell. 54(19): 9381-9394 (2024)- [j27]Songtao Liu, Jacinto Colan, Yaonan Zhu, Taisuke Kobayashi, Kazunari Misawa, Masaru Takeuchi, Yasuhisa Hasegawa:
Latent regression based model predictive control for tissue triangulation. Adv. Robotics 38(5): 283-306 (2024) - [j26]Takanori Jin, Taisuke Kobayashi, Takamitsu Matsubara:
Constrained footstep planning using model-based reinforcement learning in virtual constraint-based walking. Adv. Robotics 38(8): 525-545 (2024) - [j25]Ryoya Mori, Tadayoshi Aoyama, Taisuke Kobayashi, Kazuya Sakamoto, Masaru Takeuchi, Yasuhisa Hasegawa:
Real-Time Spatiotemporal Assistance for Micromanipulation Using Imitation Learning. IEEE Robotics Autom. Lett. 9(4): 3506-3513 (2024) - [c34]Takanori Jin, Taisuke Kobayashi, Masahiro Doi:
Real-time Detailed Self-collision Avoidance in Whole-body Model Predictive Control. Humanoids 2024: 675-681 - [c33]Taisuke Kobayashi:
Consolidated Adaptive T-soft Update for Deep Reinforcement Learning. IJCNN 2024: 1-8 - [c32]Taisuke Kobayashi, Takahito Enomoto:
Autonomous Driving of Personal Mobility by Imitation Learning from Small and Noisy Dataset. SII 2024: 404-409 - [c31]Taisuke Kobayashi, Yusuke Takeda:
Autonomous Driving from Diverse Demonstrations with Implicit Selection of Optimal Mode. SII 2024: 441-446 - [c30]Takumi Aotani, Taisuke Kobayashi:
Cooperative Transport by Manipulators with Uncertainty-Aware Model-Based Reinforcement Learning. SII 2024: 959-964 - [i30]Taisuke Kobayashi:
Revisiting Experience Replayable Conditions. CoRR abs/2402.10374 (2024) - [i29]Taisuke Kobayashi:
LiRA: Light-Robust Adversary for Model-based Reinforcement Learning in Real World. CoRR abs/2409.19617 (2024) - [i28]Wendyam Eric Lionel Ilboudo, Taisuke Kobayashi, Takamitsu Matsubara:
Domains as Objectives: Domain-Uncertainty-Aware Policy Optimization through Explicit Multi-Domain Convex Coverage Set Learning. CoRR abs/2410.04719 (2024) - [i27]Taisuke Kobayashi:
DROP: Distributional and Regular Optimism and Pessimism for Reinforcement Learning. CoRR abs/2410.17473 (2024) - [i26]Taisuke Kobayashi, Takumi Aotani:
Design of Restricted Normalizing Flow towards Arbitrary Stochastic Policy with Computational Efficiency. CoRR abs/2412.12894 (2024) - [i25]Keiichiro Takahashi, Taisuke Kobayashi, Tomoya Yamanokuchi, Takamitsu Matsubara:
Weber-Fechner Law in Temporal Difference learning derived from Control as Inference. CoRR abs/2412.21004 (2024) - 2023
- [j24]Taisuke Kobayashi, Takumi Aotani:
Design of restricted normalizing flow towards arbitrary stochastic policy with computational efficiency. Adv. Robotics 37(12): 719-736 (2023) - [j23]Taisuke Kobayashi, Ryoma Watanuki:
Sparse representation learning with modified q-VAE towards minimal realization of world model. Adv. Robotics 37(13): 807-827 (2023) - [j22]Wendyam Eric Lionel Ilboudo, Taisuke Kobayashi, Takamitsu Matsubara:
AdaTerm: Adaptive T-distribution estimated robust moments for Noise-Robust stochastic gradient optimization. Neurocomputing 557: 126692 (2023) - [c29]Wendyam Eric Lionel Ilboudo, Taisuke Kobayashi, Takamitsu Matsubara:
Domains as Objectives: Multi-Domain Reinforcement Learning with Convex-Coverage Set Learning for Domain Uncertainty Awareness. IROS 2023: 5622-5629 - [i24]Taisuke Kobayashi:
Soft Actor-Critic Algorithm with Truly Inequality Constraint. CoRR abs/2303.04356 (2023) - [i23]Taisuke Kobayashi:
Intentionally-underestimated Value Function at Terminal State for Temporal-difference Learning with Mis-designed Reward. CoRR abs/2308.12772 (2023) - 2022
- [j21]Taisuke Kobayashi, Emmanuel C. Dean-Leon, Julio Rogelio Guadarrama-Olvera, Florian Bergner, Gordon Cheng:
Whole-Body Multicontact Haptic Human-Humanoid Interaction Based on Leader-Follower Switching: A Robot Dance of the "Box Step". Adv. Intell. Syst. 4(2) (2022) - [j20]Taisuke Kobayashi, Yutaro Ikawa, Takamitsu Matsubara:
Sample-efficient gear-ratio optimization for biomechanical energy harvester. Int. J. Intell. Robotics Appl. 6(1): 10-22 (2022) - [j19]Taisuke Kobayashi, Toshiya Mabuchi, Mato Kosaka:
Light-weight behavior-based continuous authentication for personalized mobile robot. Int. J. Intell. Robotics Appl. 6(4): 694-706 (2022) - [j18]Taisuke Kobayashi:
Optimistic reinforcement learning by forward Kullback-Leibler divergence optimization. Neural Networks 152: 169-180 (2022) - [j17]Taisuke Kobayashi:
Adaptive and multiple time-scale eligibility traces for online deep reinforcement learning. Robotics Auton. Syst. 151: 104019 (2022) - [j16]Taisuke Kobayashi, Shingo Murata, Tetsunari Inamura:
Latent Representation in Human-Robot Interaction With Explicit Consideration of Periodic Dynamics. IEEE Trans. Hum. Mach. Syst. 52(5): 928-940 (2022) - [j15]Wendyam Eric Lionel Ilboudo, Taisuke Kobayashi, Kenji Sugimoto:
Robust Stochastic Gradient Descent With Student-t Distribution Based First-Order Momentum. IEEE Trans. Neural Networks Learn. Syst. 33(3): 1324-1337 (2022) - [c28]Taisuke Kobayashi:
L2C2: Locally Lipschitz Continuous Constraint towards Stable and Smooth Reinforcement Learning. IROS 2022: 4032-4039 - [i22]Wendyam Eric Lionel Ilboudo, Taisuke Kobayashi, Kenji Sugimoto:
AdaTerm: Adaptive T-Distribution Estimated Robust Moments towards Noise-Robust Stochastic Gradient Optimizer. CoRR abs/2201.06714 (2022) - [i21]Taisuke Kobayashi:
L2C2: Locally Lipschitz Continuous Constraint towards Stable and Smooth Reinforcement Learning. CoRR abs/2202.07152 (2022) - [i20]Taisuke Kobayashi:
Consolidated Adaptive T-soft Update for Deep Reinforcement Learning. CoRR abs/2202.12504 (2022) - [i19]Taisuke Kobayashi:
Proximal Policy Optimization with Adaptive Threshold for Symmetric Relative Density Ratio. CoRR abs/2203.09809 (2022) - [i18]Taisuke Kobayashi, Ryoma Watanuki:
Sparse Representation Learning with Modified q-VAE towards Minimal Realization of World Model. CoRR abs/2208.03936 (2022) - [i17]Taisuke Kobayashi, Kota Fukumoto:
Real-time Sampling-based Model Predictive Control based on Reverse Kullback-Leibler Divergence and Its Adaptive Acceleration. CoRR abs/2212.04298 (2022) - [i16]Taisuke Kobayashi:
Reward Bonuses with Gain Scheduling Inspired by Iterative Deepening Search. CoRR abs/2212.10765 (2022) - 2021
- [j14]Takumi Aotani, Taisuke Kobayashi, Kenji Sugimoto:
Meta-Optimization of Bias-Variance Trade-Off in Stochastic Model Learning. IEEE Access 9: 148783-148799 (2021) - [j13]Takumi Aotani, Taisuke Kobayashi, Kenji Sugimoto:
Bottom-up multi-agent reinforcement learning by reward shaping for cooperative-competitive tasks. Appl. Intell. 51(7): 4434-4452 (2021) - [j12]Shunki Itadera, Taisuke Kobayashi, Jun Nakanishi, Tadayoshi Aoyama, Yasuhisa Hasegawa:
Towards physical interaction-based sequential mobility assistance using latent generative model of movement state. Adv. Robotics 35(1): 64-79 (2021) - [j11]Hidehito Fujiishi, Taisuke Kobayashi, Kenji Sugimoto:
Safe and efficient imitation learning by clarification of experienced latent space. Adv. Robotics 35(16): 1012-1027 (2021) - [j10]Taisuke Kobayashi, Wendyam Eric Lionel Ilboudo:
t-soft update of target network for deep reinforcement learning. Neural Networks 136: 63-71 (2021) - [c27]Taisuke Kobayashi:
Adaptive Eligibility Traces for Online Deep Reinforcement Learning. IAS 2021: 417-428 - [c26]Taisuke Kobayashi:
Proximal Policy Optimization with Relative Pearson Divergence. ICRA 2021: 8416-8421 - [c25]Wendyam Eric Lionel Ilboudo, Taisuke Kobayashi, Kenji Sugimoto:
Adaptive t-Momentum-based Optimization for Unknown Ratio of Outliers in Amateur Data in Imitation Learning. IROS 2021: 7851-7857 - [c24]Tobias Betz, Hidehito Fujiishi, Taisuke Kobayashi:
Behavioral Cloning from Observation with Bi-directional Dynamics Model. SII 2021: 184-189 - [c23]Taisuke Kobayashi:
Towards Deep Robot Learning with Optimizer applicable to Non-stationary Problems. SII 2021: 190-194 - [d1]Taisuke Kobayashi:
Hexapod locomotion with CPG. IEEE DataPort, 2021
- [i15]Taisuke Kobayashi:
Mirror-Descent Inverse Kinematics for Box-constrained Joint Space. CoRR abs/2101.07625 (2021) - [i14]Taisuke Kobayashi, Yutaro Ikawa, Takamitsu Matsubara:
Sample-efficient Gear-ratio Optimization for Biomechanical Energy Harvester. CoRR abs/2104.00382 (2021) - [i13]Taisuke Kobayashi, Kenta Yoshizawa:
Optimization Algorithm for Feedback and Feedforward Policies towards Robot Control Robust to Sensing Failures. CoRR abs/2104.00385 (2021) - [i12]Taisuke Kobayashi:
Optimistic Reinforcement Learning by Forward Kullback-Leibler Divergence Optimization. CoRR abs/2105.12991 (2021) - [i11]Taisuke Kobayashi:
Hyperbolically-Discounted Reinforcement Learning on Reward-Punishment Framework. CoRR abs/2106.01516 (2021) - [i10]Taisuke Kobayashi, Shingo Murata, Tetsunari Inamura:
Latent Representation in Human-Robot Interaction with Explicit Consideration of Periodic Dynamics. CoRR abs/2106.08531 (2021) - [i9]Wendyam Eric Lionel Ilboudo, Taisuke Kobayashi, Kenji Sugimoto:
Adaptive t-Momentum-based Optimization for Unknown Ratio of Outliers in Amateur Data in Imitation Learning. CoRR abs/2108.00625 (2021) - [i8]Taisuke Kobayashi, Takahito Enomoto:
Towards Autonomous Driving of Personal Mobility with Small and Noisy Dataset using Tsallis-statistics-based Behavioral Cloning. CoRR abs/2111.14294 (2021) - 2020
- [j9]Taisuke Kobayashi, Toshiki Sugino:
Reinforcement learning for quadrupedal locomotion with design of continual-hierarchical curriculum. Eng. Appl. Artif. Intell. 95: 103869 (2020) - [j8]Taisuke Kobayashi:
q-VAE for Disentangled Representation Learning and Latent Dynamical Systems. IEEE Robotics Autom. Lett. 5(4): 5669-5676 (2020) - [i7]Wendyam Eric Lionel Ilboudo, Taisuke Kobayashi, Kenji Sugimoto:
TAdam: A Robust Stochastic Gradient Optimizer. CoRR abs/2003.00179 (2020) - [i6]Taisuke Kobayashi:
q-VAE for Disentangled Representation Learning and Latent Dynamical Systems. CoRR abs/2003.01852 (2020) - [i5]Taisuke Kobayashi:
Towards Deep Robot Learning with Optimizer applicable to Non-stationary Problems. CoRR abs/2007.15890 (2020) - [i4]Taisuke Kobayashi:
Adaptive and Multiple Time-scale Eligibility Traces for Online Deep Reinforcement Learning. CoRR abs/2008.10040 (2020) - [i3]Taisuke Kobayashi, Wendyam Eric Lionel Ilboudo:
t-Soft Update of Target Network for Deep Reinforcement Learning. CoRR abs/2008.10861 (2020) - [i2]Taisuke Kobayashi:
Proximal Policy Optimization with Relative Pearson Divergence. CoRR abs/2010.03290 (2020) - [i1]Koki Kobayashi, Masaki Ogura, Taisuke Kobayashi, Kenji Sugimoto:
Deep unfolding-based output feedback control design for linear systems with input saturation. CoRR abs/2011.10196 (2020)
2010 – 2019
- 2019
- [j7]Taisuke Kobayashi:
Student-t policy in reinforcement learning to acquire global optimum of robot control. Appl. Intell. 49(12): 4335-4347 (2019) - [c22]Taisuke Kobayashi, Emmanuel C. Dean-Leon, Julio Rogelio Guadarrama-Olvera, Florian Bergner, Gordon Cheng:
Multi-Contacts Force-Reactive Walking Control during Physical Human-Humanoid Interaction. Humanoids 2019: 33-39 - [c21]Taisuke Kobayashi, Toshiki Sugino:
Continual Learning Exploiting Structure of Fractal Reservoir Computing. ICANN (Workshop) 2019: 35-47 - [c20]Taisuke Kobayashi:
Variational Deep Embedding with Regularized Student-t Mixture Model. ICANN (3) 2019: 443-455 - [c19]Taisuke Kobayashi, Takumi Aotani, Julio Rogelio Guadarrama-Olvera, Emmanuel C. Dean-Leon, Gordon Cheng:
Reward-Punishment Actor-Critic Algorithm Applying to Robotic Non-grasping Manipulation. ICDL-EPIROB 2019: 37-42 - [c18]Shunki Itadera, Taisuke Kobayashi, Jun Nakanishi, Tadayoshi Aoyama, Yasuhisa Hasegawa:
Impedance Control based Assistive Mobility Aid through Online Classification of User's State. SII 2019: 243-248 - 2018
- [j6]Taisuke Kobayashi, Kosuke Sekiyama, Yasuhisa Hasegawa, Tadayoshi Aoyama, Toshio Fukuda:
Unified bipedal gait for autonomous transition between walking and running in pursuit of energy minimization. Robotics Auton. Syst. 103: 27-41 (2018) - [c17]Yutaro Ikawa, Taisuke Kobayashi, Takamitsu Matsubara:
Biomechanical Energy Harvester with Continuously Variable Transmission: Prototyping and Preliminary Evaluation. AIM 2018: 1045-1050 - [c16]Taisuke Kobayashi:
Practical Fractional-Order Neuron Dynamics for Reservoir Computing. ICANN (3) 2018: 116-125 - [c15]Taisuke Kobayashi:
Check Regularization: Combining Modularity and Elasticity for Memory Consolidation. ICANN (2) 2018: 315-325 - [c14]Takumi Aotani, Taisuke Kobayashi, Kenji Sugimoto:
Bottom-up Multi-agent Reinforcement Learning for Selective Cooperation. SMC 2018: 3590-3595 - 2016
- [j5]Taisuke Kobayashi, Kosuke Sekiyama, Tadayoshi Aoyama, Yasuhisa Hasegawa, Toshio Fukuda:
Selection of two arm-swing strategies for bipedal walking to enhance both stability and efficiency. Adv. Robotics 30(6): 386-401 (2016) - [c13]Taisuke Kobayashi, Yasuhisa Hasegawa, Kosuke Sekiyama, Tadayoshi Aoyama, Toshio Fukuda:
Unified bipedal gait for walking and running by dynamics-based virtual holonomic constraint in PDAC. ICRA 2016: 1769-1775 - [c12]Taisuke Kobayashi, Kosuke Sekiyama, Yasuhisa Hasegawa, Tadayoshi Aoyama, Toshio Fukuda:
Quasi-passive dynamic autonomous control to enhance horizontal and turning gait speed control. IROS 2016: 5612-5617 - [c11]Tomoro Ota, Kenichi Ohara, Akihiko Ichikawa, Taisuke Kobayashi, Yasuhisa Hasegawa, Toshio Fukuda:
Modeling of the high-speed running humanoid robot. MHS 2016: 1-3 - 2015
- [j4]Taisuke Kobayashi, Kosuke Sekiyama, Tadayoshi Aoyama, Toshio Fukuda:
Cane-supported walking by humanoid robot and falling-factor-based optimal cane usage selection. Robotics Auton. Syst. 68: 21-35 (2015) - [j3]Zhiguo Lu, Kosuke Sekiyama, Tadayoshi Aoyama, Yasuhisa Hasegawa, Taisuke Kobayashi, Toshio Fukuda:
Energetically Efficient Ladder Descent Motion With Internal Stress and Body Motion Optimized for a Multilocomotion Robot. IEEE Trans. Ind. Electron. 62(8): 4972-4984 (2015) - [j2]Taisuke Kobayashi, Tadayoshi Aoyama, Kosuke Sekiyama, Toshio Fukuda:
Selection Algorithm for Locomotion Based on the Evaluation of Falling Risk. IEEE Trans. Robotics 31(3): 750-765 (2015) - [c10]Taisuke Kobayashi, Kosuke Sekiyama, Tadayoshi Aoyama, Yasuhisa Hasegawa, Toshio Fukuda:
Optimal use of arm-swing for bipedal walking control. ICRA 2015: 5698-5703 - [c9]Taisuke Kobayashi, Tadayoshi Aoyama, Yasuhisa Hasegawa, Kosuke Sekiyama, Toshio Fukuda:
Dynamics-based virtual holonomic constraint for PDAC running. MHS 2015: 1-3 - 2014
- [c8]Taisuke Kobayashi, Kosuke Sekiyama, Tadayoshi Aoyama, Yasuhisa Hasegawa, Toshio Fukuda:
Optimal selection of cane usage with humanoid robot. Humanoids 2014: 199-204 - [c7]Taisuke Kobayashi, Kosuke Sekiyama, Tadayoshi Aoyama, Yasuhisa Hasegawa, Toshio Fukuda:
Support of COG trajectory tracking by arm-swing with bipedal walking. MHS 2014: 1-3 - 2013
- [c6]Taisuke Kobayashi, Tadayoshi Aoyama, Masafumi Sobajima, Kosuke Sekiyama, Toshio Fukuda:
Locomotion selection strategy for multi-locomotion robot based on stability and efficiency. IROS 2013: 2616-2621 - [c5]Taisuke Kobayashi, Tadayoshi Aoyama, Masafumi Sobajima, Kosuke Sekiyama, Toshio Fukuda:
Bipedal walking by humanoid robot with cane - Preventive usage of cane based on impulse force. MHS 2013: 1-6 - 2012
- [c4]Tadayoshi Aoyama, Kosuke Sekiyama, Zhiguo Lu, Taisuke Kobayashi, Yasuhisa Hasegawa, Toshio Fukuda:
Stability enhancement of 3-D biped walking based on Passive Dynamic Autonomous Control. Humanoids 2012: 443-448 - [c3]Zhiguo Lu, Kosuke Sekiyama, Tadayoshi Aoyama, Yasuhisa Hasegawa, Taisuke Kobayashi, Toshio Fukuda:
Optimal control of energetically efficient ladder decent motion with internal stress adjustment using key joint method. IROS 2012: 2216-2221 - [c2]Taisuke Kobayashi, Tadayoshi Aoyama, Kosuke Sekiyama, Zhiguo Lu, Yasuhisa Hasegawa, Toshio Fukuda:
Locomotion selection of Multi-Locomotion Robot based on Falling Risk and moving efficiency. IROS 2012: 2869-2874 - [c1]Taisuke Kobayashi, Tadayoshi Aoyama, Kosuke Sekiyama, Toshio Fukuda:
Stabilization and moving efficiency improvement by adjustment of moving speed in single locomotion. MHS 2012: 325-330
1990 – 1999
- 1994
- [j1]Makoto Akeo, Hiroshi Hashimoto, Taisuke Kobayashi, Tetsuo Shibusawa:
Computer Graphics System for Reproducing Three-dimensional Shape from Idea Sketch. Comput. Graph. Forum 13(3): 477-488 (1994)
Coauthor Index
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