Robotics

Building sample-efficient agents that generalize out-of-distribution (OOD) in real-world settings remains a fundamental unsolved problem on the path towards achieving higher-level cognition. One particularly promising approach is to begin with low-dimensional, pretrained representations of our world, which should facilitateefficient downstream learning and generalization. By training 240 representations

Microphone and speaker array designs have increasingly diverged from simple topologies due to diversity of physical host geometries and use cases. Effective beamformer design must now account for variation in the array’s acoustic radiation pattern, spatial distribution of target and noise sources, and intended beampattern directivity. Relevant tasks such as representing complex pressure fields, specifying

As more compact designs and more assembled function modules are utilized in modern electronic devices, radiofrequency interference (RFI) source reconstruction is becoming more challenging because different noise sources may contribute simultaneously. This article presents a novel methodology to reconstruct multiple random noise sources on a real-world product, including several double-data-rate (DDR) memory

Language-guided robots performing home and office tasks must navigate in and interact with the world. Grounding language instructions against visual observations and actions to take in an environment is an open challenge. We present Embodied BERT (EmBERT), a transformer-based model which can attend to high-dimensional, multi-modal inputs across long temporal horizons for language-conditioned task completion

The recent rapid progress of deep learning algorithms in generating realistic images, especially in Generative Adversarial Networks (GAN) and Variational Auto-Encoders (VAE), has helped advance new applications. Examples of such applications range from generating and manipulating new synthetic data for self-driving cars, to building/urban architectures, to interior design, and gaming. Furthermore, several