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Full-Text Articles in Computer Engineering
Robot Learning To Pour Solid Objects Accurately, Juan Wilches, Yu Sun
Robot Learning To Pour Solid Objects Accurately, Juan Wilches, Yu Sun
36th Florida Conference on Recent Advances in Robotics
Pouring is an efficient way to transfer objects from
one container to another. This abstract summarizes a method
to accurately pour solid objects, such as ice cubes. It leverages
visual and proprioceptive feedback together with contextual
information to control the forward and backward rotation of the
pouring container. These feedback signals are fed to a recurrent
neural network that produces the control signal. The proposed
approach can achieve a human-like pouring accuracy in both a
simulation and a real setup.
Learning State-Dependent Sensor Measurement Models To Improve Robot Localization Accuracy, Troi André Williams
Learning State-Dependent Sensor Measurement Models To Improve Robot Localization Accuracy, Troi André Williams
USF Tampa Graduate Theses and Dissertations
This dissertation proposes a novel method called state-dependent sensor measurement models (SDSMMs). Such models dynamically predict the state-dependent bias and uncertainty of sensor measurements, ultimately improving fundamental robot tasks such as localization. In our first investigation, we introduced the state-dependent sensor measurement model framework, described their properties, stated the input and output of these models, and described how to train them. We also explained how to integrate such models with an Extended Kalman Filter and a Particle Filter, two popular robot state estimation algorithms. We validated the proposed framework through a series of localization tasks. The results showed that our …