Reinforcement learning (RL), particularly its combination with deep neural networks,
referred to as deep RL (DRL), has shown tremendous promise across a wide range of …

There has been an emerging paradigm shift from the era of “internet AI” to “embodied AI,”
where AI algorithms and agents no longer learn from datasets of images, videos or text …

Abstract We introduce Habitat 2.0 (H2. 0), a simulation platform for training virtual robots in
interactive 3D environments and complex physics-enabled scenarios. We make …

Semantic navigation is necessary to deploy mobile robots in uncontrolled environments
such as homes or hospitals. Many learning-based approaches have been proposed in …

Contrastive language image pretraining (CLIP) encoders have been shown to be beneficial
for a range of visual tasks from classification and detection to captioning and image …

Robotic learning for navigation in unfamiliar environments needs to provide policies for both
task-oriented navigation (ie, reaching a goal that the robot has located), and task-agnostic …

The recent advancements in machine learning and artificial intelligence (particularly
foundation models such as BERT, GPT-3, T5, ResNet, etc.) have demonstrated remarkable …

The virtual-to-real paradigm, ie, training models on virtual data and then applying them to
solve real-world problems, has attracted more and more attention from various domains by …

General-purpose pre-trained models (" foundation models") have enabled practitioners to
produce generalizable solutions for individual machine learning problems with datasets that …

Abstract We introduce SoundSpaces 2.0, a platform for on-the-fly geometry-based audio
rendering for 3D environments. Given a 3D mesh of a real-world environment …