Tatsuya Matsushima

Orcid: 0000-0002-1537-7770

According to our database1, Tatsuya Matsushima authored at least 17 papers between 2018 and 2024.

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Bibliography

2024
Special issue on real-world robot applications of the foundation models.
Adv. Robotics, September, 2024

Real-world robot applications of foundation models: a review.
Adv. Robotics, September, 2024

Self-Recovery Prompting: Promptable General Purpose Service Robot System with Foundation Models and Self-Recovery.
Proceedings of the IEEE International Conference on Robotics and Automation, 2024

Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration.
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Proceedings of the IEEE International Conference on Robotics and Automation, 2024

GenDOM: Generalizable One-shot Deformable Object Manipulation with Parameter-Aware Policy.
Proceedings of the IEEE International Conference on Robotics and Automation, 2024

2023
Collective Intelligence for 2D Push Manipulations With Mobile Robots.
IEEE Robotics Autom. Lett., May, 2023

TRAIL Team Description Paper for RoboCup@Home 2023.
CoRR, 2023

GenORM: Generalizable One-shot Rope Manipulation with Parameter-Aware Policy.
CoRR, 2023

2022
Collective Intelligence for Object Manipulation with Mobile Robots.
CoRR, 2022

World robot challenge 2020 - partner robot: a data-driven approach for room tidying with mobile manipulator.
Adv. Robotics, 2022

2021
Tool as Embodiment for Recursive Manipulation.
CoRR, 2021

Identifying Co-Adaptation of Algorithmic and Implementational Innovations in Deep Reinforcement Learning: A Taxonomy and Case Study of Inference-based Algorithms.
CoRR, 2021

Co-Adaptation of Algorithmic and Implementational Innovations in Inference-based Deep Reinforcement Learning.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Policy Information Capacity: Information-Theoretic Measure for Task Complexity in Deep Reinforcement Learning.
Proceedings of the 38th International Conference on Machine Learning, 2021

Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimization.
Proceedings of the 9th International Conference on Learning Representations, 2021

2020
Modeling Task Uncertainty for Safe Meta-Imitation Learning.
Frontiers Robotics AI, 2020

2018
Neuron as an Agent.
Proceedings of the 6th International Conference on Learning Representations, 2018


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