28 research outputs found
Meta Reinforcement Learning for Sim-to-real Domain Adaptation
Modern reinforcement learning methods suffer from low sample efficiency and
unsafe exploration, making it infeasible to train robotic policies entirely on
real hardware. In this work, we propose to address the problem of sim-to-real
domain transfer by using meta learning to train a policy that can adapt to a
variety of dynamic conditions, and using a task-specific trajectory generation
model to provide an action space that facilitates quick exploration. We
evaluate the method by performing domain adaptation in simulation and analyzing
the structure of the latent space during adaptation. We then deploy this policy
on a KUKA LBR 4+ robot and evaluate its performance on a task of hitting a
hockey puck to a target. Our method shows more consistent and stable domain
adaptation than the baseline, resulting in better overall performance.Comment: Submitted to ICRA 202
Alpha Net: Adaptation with Composition in Classifier Space
Deep learning classification models typically train poorly on classes with
small numbers of examples. Motivated by the human ability to solve this task,
models have been developed that transfer knowledge from classes with many
examples to learn classes with few examples. Critically, the majority of these
models transfer knowledge within model feature space. In this work, we
demonstrate that transferring knowledge within classified space is more
effective and efficient. Specifically, by linearly combining strong nearest
neighbor classifiers along with a weak classifier, we are able to compose a
stronger classifier. Uniquely, our model can be implemented on top of any
existing classification model that includes a classifier layer. We showcase the
success of our approach in the task of long-tailed recognition, whereby the
classes with few examples, otherwise known as the "tail" classes, suffer the
most in performance and are the most challenging classes to learn. Using
classifier-level knowledge transfer, we are able to drastically improve - by a
margin as high as 12.6% - the state-of-the-art performance on the "tail"
categories.Comment: Under revie