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 …

Deep reinforcement learning has recently seen huge success across multiple areas in the
robotics domain. Owing to the limitations of gathering real-world data, ie, sample inefficiency …

We introduce SceneDiffuser, a conditional generative model for 3D scene understanding.
SceneDiffuser provides a unified model for solving scene-conditioned generation …

We present the largest and most comprehensive empirical study of pre-trained visual
representations (PVRs) or visual 'foundation models' for Embodied AI. First, we curate …

Building a robot that can understand and learn to interact by watching humans has inspired
several vision problems. However, despite some successful results on static datasets, it …

In this work, we present a scalable reinforcement learning method for training multi-task
policies from large offline datasets that can leverage both human demonstrations and …

Recently, zero-shot and few-shot learning via Contrastive Vision-Language Pre-training
(CLIP) have shown inspirational performance on 2D visual recognition, which learns to …

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

Designing an efficient yet deployment-friendly 3D backbone to handle sparse point clouds is
a fundamental problem in 3D perception. Compared with the customized sparse …

Navigation is a fundamental problem of mobile robots, for which Deep Reinforcement
Learning (DRL) has received significant attention because of its strong representation and …