Mathew Monfort

Goal-predictive robotic teleoperation from noisy sensors

Robotic teleoperation from a human operators pose demonstrations provides an intuitive and effective means of control that has been made feasible by improvements in sensor technologies in recent years. However, the imprecision of low-cost depth cameras and the difficulty of calibrating a frame of reference for the operator introduce inefficiencies in this process when performing tasks that require interactions with objects in the robots workspace. We develop a goal-predictive teleoperation system that aids in “de-noising” the controls of the operator to be more goal-directed. Our approach uses inverse optimal control to predict the intended object of interaction from the current motion trajectory in real time and then adapts the degree of autonomy between the operators demonstrations and autonomous completion of the predicted task. We evaluate our approach using the Microsoft Kinect depth camera as our input sensor to control a Rethink Robotics Baxter robot.

A deep learning approach to identifying shock locations in turbulent combustion tensor fields

We introduce a deep learning approach for the identification of shock locations in large scale tensor field datasets. Such datasets are typically generated by turbulent combustion simulations. In this proof of concept approach, we use deep learning to learn mappings from strain tensors to Schlieren images which serve as labels. The use of neural networks allows for the Schlieren values to be approximated more efficiently than calculating the values from the density gradient. In addition, we show that this approach can be used to predict the Schlieren values for both two-dimensional and three-dimensional tensor fields, potentially allowing for anomaly detection in tensor flows. Results on two shock example datasets show that this approach can assist in the extraction of features from reacting flow tensor fields.