Visuotactile-RL: Learning Multimodal Manipulation Policies with Deep Reinforcement Learning

Author:

Hansen Johanna1,Hogan Francois1,Rivkin Dmitriy1,Meger David1,Jenkin Michael1,Dudek Gregory1

Affiliation:

1. Samsung AI Research Center Montreal,Montreal,QC,Canada

Publisher

IEEE

Reference36 articles.

1. A unified approach for motion and force control of robot manipulators: The operational space formulation;khatib;IEEE J Robotics Autom,1987

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5. Addressing function approximation error in actor-critic methods;fujimoto;International Conference on Machine Learning,0

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1. Bi-Touch: Bimanual Tactile Manipulation With Sim-to-Real Deep Reinforcement Learning;IEEE Robotics and Automation Letters;2023-09

2. Leveraging Multi-modal Sensing for Robotic Insertion Tasks in R&D Laboratories;2023 IEEE 19th International Conference on Automation Science and Engineering (CASE);2023-08-26

3. Safe Self-Supervised Learning in Real of Visuo-Tactile Feedback Policies for Industrial Insertion;2023 IEEE International Conference on Robotics and Automation (ICRA);2023-05-29

4. Multimodal Time Series Learning of Robots Based on Distributed and Integrated Modalities: Verification with a Simulator and Actual Robots;2023 IEEE International Conference on Robotics and Automation (ICRA);2023-05-29

5. Learning positioning policies for mobile manipulation operations with deep reinforcement learning;International Journal of Machine Learning and Cybernetics;2023-03-17

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