@inproceedings{201f0b31b2104608a78a77120da80a26,
title = "DRL Based Intelligent Joint Manipulator and Viewing Camera Control for Reaching Tasks and Environments with Obstacles and Occluders",
abstract = "This work studies joint camera and robotic manipulator control for reaching tasks in complex environments with obstacles and occluders. We obviate the conventional challenges involved in complex perception, planning, and control modules and careful calibration for sensing and actuation and seek a solution leveraging deep reinforcement learning (DRL). Our method using DRL and deep Q-learning learns a policy for robot actuation and perception control, mapping directly raw image pixels inputs into camera motion and manipulator joint control actions outputs. We show results comparing different training approaches, and demonstrating competency for increasingly complex situations and degrees of freedom. These preliminary experiments suggest the effectiveness and robustness of the proposed approach.",
keywords = "Deep Q -learning, deep reinforcement learning, joint actuation and perception control",
author = "Staley, {Edward W.} and Katyal, {Kapil D.} and Philippe Burlina",
year = "2018",
month = oct,
day = "10",
doi = "10.1109/IJCNN.2018.8489273",
language = "English (US)",
series = "Proceedings of the International Joint Conference on Neural Networks",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings",
note = "2018 International Joint Conference on Neural Networks, IJCNN 2018 ; Conference date: 08-07-2018 Through 13-07-2018",
}