1,159 research outputs found
Vision-Based Multi-Task Manipulation for Inexpensive Robots Using End-To-End Learning from Demonstration
We propose a technique for multi-task learning from demonstration that trains
the controller of a low-cost robotic arm to accomplish several complex picking
and placing tasks, as well as non-prehensile manipulation. The controller is a
recurrent neural network using raw images as input and generating robot arm
trajectories, with the parameters shared across the tasks. The controller also
combines VAE-GAN-based reconstruction with autoregressive multimodal action
prediction. Our results demonstrate that it is possible to learn complex
manipulation tasks, such as picking up a towel, wiping an object, and
depositing the towel to its previous position, entirely from raw images with
direct behavior cloning. We show that weight sharing and reconstruction-based
regularization substantially improve generalization and robustness, and
training on multiple tasks simultaneously increases the success rate on all
tasks
Contrastive Learning for Unsupervised Domain Adaptation of Time Series
Unsupervised domain adaptation (UDA) aims at learning a machine learning
model using a labeled source domain that performs well on a similar yet
different, unlabeled target domain. UDA is important in many applications such
as medicine, where it is used to adapt risk scores across different patient
cohorts. In this paper, we develop a novel framework for UDA of time series
data, called CLUDA. Specifically, we propose a contrastive learning framework
to learn contextual representations in multivariate time series, so that these
preserve label information for the prediction task. In our framework, we
further capture the variation in the contextual representations between source
and target domain via a custom nearest-neighbor contrastive learning. To the
best of our knowledge, ours is the first framework to learn domain-invariant,
contextual representation for UDA of time series data. We evaluate our
framework using a wide range of time series datasets to demonstrate its
effectiveness and show that it achieves state-of-the-art performance for time
series UDA.Comment: Published as a conference paper at ICLR 202
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