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Creators/Authors contains: "Huang, Baichuan"

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  1. ; ; ; ( , IEEE International Conference on Robotics and Automation)
    Free, publicly-accessible full text available January 1, 2023
  2. ; ; ; ( , IEEE International Conference on Robotics and Automation)
    Free, publicly-accessible full text available January 1, 2023
  3. ; ; ; ( , IEEE Robotics and Automation Letters)
    Free, publicly-accessible full text available January 1, 2023
  4. ; ; ; ; ( , IEEE International Conference on Robotics and Automation)
    Free, publicly-accessible full text available January 1, 2023
  5. ; ; ; ( , Proceedings of the 2021 International Conference on Robotics and Automation (ICRA))
    Accepted Manuscript Full Text Available
  6. ; ; ; ; ; ; ( , Proceedings of the 17th IEEE International Conference on Automation Science and Engineering)
    Accepted Manuscript Full Text Available
  7. ; ; ; ( , IEEE International Conference on Robotics and Automation)
    We propose a Deep Interaction Prediction Net- work (DIPN) for learning to predict complex interactions that ensue as a robot end-effector pushes multiple objects, whose physical properties, including size, shape, mass, and friction coefficients may be unknown a priori. DIPN “imagines” the effect of a push action and generates an accurate synthetic image of the predicted outcome. DIPN is shown to be sample efficient when trained in simulation or with a real robotic system. The high accuracy of DIPN allows direct integration with a grasp network, yielding a robotic manipulation system capable of executing challenging clutter removal tasks while beingmore »trained in a fully self-supervised manner. The overall network demonstrates intelligent behavior in selecting proper actions between push and grasp for completing clutter removal tasks and significantly outperforms the previous state-of-the- art. Remarkably, DIPN achieves even better performance on the real robotic hardware system than in simulation.« less
    Accepted Manuscript Full Text Available