2 research outputs found
Adapt On-the-Go: Behavior Modulation for Single-Life Robot Deployment
To succeed in the real world, robots must cope with situations that differ
from those seen during training. We study the problem of adapting on-the-fly to
such novel scenarios during deployment, by drawing upon a diverse repertoire of
previously learned behaviors. Our approach, RObust Autonomous Modulation
(ROAM), introduces a mechanism based on the perceived value of pre-trained
behaviors to select and adapt pre-trained behaviors to the situation at hand.
Crucially, this adaptation process all happens within a single episode at test
time, without any human supervision. We provide theoretical analysis of our
selection mechanism and demonstrate that ROAM enables a robot to adapt rapidly
to changes in dynamics both in simulation and on a real Go1 quadruped, even
successfully moving forward with roller skates on its feet. Our approach adapts
over 2x as efficiently compared to existing methods when facing a variety of
out-of-distribution situations during deployment by effectively choosing and
adapting relevant behaviors on-the-fly.Comment: 19 pages, 6 figure
Machine Learning Models for Abnormality Detection in Musculoskeletal Radiographs
Increasing radiologist workloads and increasing primary care radiology services make it relevant to explore the use of artificial intelligence (AI) and particularly deep learning to provide diagnostic assistance to radiologists and primary care physicians in improving the quality of patient care. This study investigates new model architectures and deep transfer learning to improve the performance in detecting abnormalities of upper extremities while training with limited data. DenseNet-169, DenseNet-201, and InceptionResNetV2 deep learning models were implemented and evaluated on the humerus and finger radiographs from MURA, a large public dataset of musculoskeletal radiographs. These architectures were selected because of their high recognition accuracy in a benchmark study. The DenseNet-201 and InceptionResNetV2 models, employing deep transfer learning to optimize training on limited data, detected abnormalities in the humerus radiographs with 95% CI accuracies of 83–92% and high sensitivities greater than 0.9, allowing for these models to serve as useful initial screening tools to prioritize studies for expedited review. The performance in the case of finger radiographs was not as promising, possibly due to the limitations of large inter-radiologist variation. It is suggested that the causes of this variation be further explored using machine learning approaches, which may lead to appropriate remediation