1,290 research outputs found
Robot Autonomy for Surgery
Autonomous surgery involves having surgical tasks performed by a robot
operating under its own will, with partial or no human involvement. There are
several important advantages of automation in surgery, which include increasing
precision of care due to sub-millimeter robot control, real-time utilization of
biosignals for interventional care, improvements to surgical efficiency and
execution, and computer-aided guidance under various medical imaging and
sensing modalities. While these methods may displace some tasks of surgical
teams and individual surgeons, they also present new capabilities in
interventions that are too difficult or go beyond the skills of a human. In
this chapter, we provide an overview of robot autonomy in commercial use and in
research, and present some of the challenges faced in developing autonomous
surgical robots
Curriculum Learning for Handwritten Text Line Recognition
Recurrent Neural Networks (RNN) have recently achieved the best performance
in off-line Handwriting Text Recognition. At the same time, learning RNN by
gradient descent leads to slow convergence, and training times are particularly
long when the training database consists of full lines of text. In this paper,
we propose an easy way to accelerate stochastic gradient descent in this
set-up, and in the general context of learning to recognize sequences. The
principle is called Curriculum Learning, or shaping. The idea is to first learn
to recognize short sequences before training on all available training
sequences. Experiments on three different handwritten text databases (Rimes,
IAM, OpenHaRT) show that a simple implementation of this strategy can
significantly speed up the training of RNN for Text Recognition, and even
significantly improve performance in some cases
AI based Robot Safe Learning and Control
Introduction This open access book mainly focuses on the safe control of robot manipulators. The control schemes are mainly developed based on dynamic neural network, which is an important theoretical branch of deep reinforcement learning. In order to enhance the safety performance of robot systems, the control strategies include adaptive tracking control for robots with model uncertainties, compliance control in uncertain environments, obstacle avoidance in dynamic workspace. The idea for this book on solving safe control of robot arms was conceived during the industrial applications and the research discussion in the laboratory. Most of the materials in this book are derived from the authors’ papers published in journals, such as IEEE Transactions on Industrial Electronics, neurocomputing, etc. This book can be used as a reference book for researcher and designer of the robotic systems and AI based controllers, and can also be used as a reference book for senior undergraduate and graduate students in colleges and universities
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Impedance learning for robots interacting with unknown environments
In this paper, impedance learning is investigated for robots interacting with unknown environments. A twoloop control framework is employed and adaptive control is developed for the inner-loop position control. The environments are described as time-varying systems with unknown parameters in the state-space form. The gradient-following scheme and betterment scheme are employed to obtain a desired impedance model, subject to unknown environments. The desired interaction performance is achieved in the sense that a defined cost function is minimized. Simulation and experiment studies are carried out to verify the validity of the proposed method
Development of a sensor coordinated kinematic model for neural network controller training
A robotic benchmark problem useful for evaluating alternative neural network controllers is presented. Specifically, it derives two camera models and the kinematic equations of a multiple degree of freedom manipulator whose end effector is under observation. The mapping developed include forward and inverse translations from binocular images to 3-D target position and the inverse kinematics of mapping point positions into manipulator commands in joint space. Implementation is detailed for a three degree of freedom manipulator with one revolute joint at the base and two prismatic joints on the arms. The example is restricted to operate within a unit cube with arm links of 0.6 and 0.4 units respectively. The development is presented in the context of more complex simulations and a logical path for extension of the benchmark to higher degree of freedom manipulators is presented
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