254,080 research outputs found
Deep Haptic Model Predictive Control for Robot-Assisted Dressing
Robot-assisted dressing offers an opportunity to benefit the lives of many
people with disabilities, such as some older adults. However, robots currently
lack common sense about the physical implications of their actions on people.
The physical implications of dressing are complicated by non-rigid garments,
which can result in a robot indirectly applying high forces to a person's body.
We present a deep recurrent model that, when given a proposed action by the
robot, predicts the forces a garment will apply to a person's body. We also
show that a robot can provide better dressing assistance by using this model
with model predictive control. The predictions made by our model only use
haptic and kinematic observations from the robot's end effector, which are
readily attainable. Collecting training data from real world physical
human-robot interaction can be time consuming, costly, and put people at risk.
Instead, we train our predictive model using data collected in an entirely
self-supervised fashion from a physics-based simulation. We evaluated our
approach with a PR2 robot that attempted to pull a hospital gown onto the arms
of 10 human participants. With a 0.2s prediction horizon, our controller
succeeded at high rates and lowered applied force while navigating the garment
around a persons fist and elbow without getting caught. Shorter prediction
horizons resulted in significantly reduced performance with the sleeve catching
on the participants' fists and elbows, demonstrating the value of our model's
predictions. These behaviors of mitigating catches emerged from our deep
predictive model and the controller objective function, which primarily
penalizes high forces.Comment: 8 pages, 12 figures, 1 table, 2018 IEEE International Conference on
Robotics and Automation (ICRA
Model predictive power control of a heat pipe cooled reactor
Heat pipe cooled reactor (HPR) has broad application prospects in deep space exploration, deep-sea submarine exploration, and other scenarios due to the small size, high inherent safety, and easy modularization and expansion. However, the HPR conducts thermal energy through evaporation and condensation of the working fluid inside the heat pipe. This feature makes the HPR a large time-delay system. If the power control system adopts the conventional PID algorithm, there will be a long settling time. Therefore, the model predictive control algorithm is proposed for the power control system to improve the control performance. The HPR linear model, which is developed by linearization of its nonlinear model, is chosen as the predictive model. The optimal control value is obtained by solving the optimization problem based on the predictive model and the electric power feedback value. The discrepancy between the predive model and the actual system response results in the presence of steady-state error. To solve this problem, an integral controller is added to eliminate the error. The appropriate control system parameters are tuned by the trial and error method. The proposed control system has satisfactory control performance, which can significantly shorten the settling time. The model predictive control can effectively overcome the influence of the large time-delay characteristic
Explaining Predictive Uncertainty with Information Theoretic Shapley Values
Researchers in explainable artificial intelligence have developed numerous
methods for helping users understand the predictions of complex supervised
learning models. By contrast, explaining the of model
outputs has received relatively little attention. We adapt the popular Shapley
value framework to explain various types of predictive uncertainty, quantifying
each feature's contribution to the conditional entropy of individual model
outputs. We consider games with modified characteristic functions and find deep
connections between the resulting Shapley values and fundamental quantities
from information theory and conditional independence testing. We outline
inference procedures for finite sample error rate control with provable
guarantees, and implement an efficient algorithm that performs well in a range
of experiments on real and simulated data. Our method has applications to
covariate shift detection, active learning, feature selection, and active
feature-value acquisition
Deep Historical Borrowing Framework to Prospectively and Simultaneously Synthesize Control Information in Confirmatory Clinical Trials with Multiple Endpoints
In current clinical trial development, historical information is receiving
more attention as providing value beyond sample size calculation.
Meta-analytic-predictive (MAP) priors and robust MAP priors have been proposed
for prospectively borrowing historical data on a single endpoint. To
simultaneously synthesize control information from multiple endpoints in
confirmatory clinical trials, we propose to approximate posterior probabilities
from a Bayesian hierarchical model and estimate critical values by deep
learning to construct pre-specified decision functions before the trial
conduct. Simulation studies and a case study demonstrate that our method
additionally preserves power, and has a satisfactory performance under
prior-data conflict
- …