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

We present Neural A*, a novel data-driven search method for path planning problems.
Despite the recent increasing attention to data-driven path planning, machine learning …

Imitation learning (IL) aims to extract knowledge from human experts' demonstrations or
artificially created agents to replicate their behaviors. It promotes interdisciplinary …

Abstract We present Vision-based Navigation with Language-based Assistance (VNLA), a
grounded vision-language task where an agent with visual perception is guided via …

We consider imitation learning problems where the learner's ability to mimic the expert
increases throughout the course of an episode as more information is revealed. One …

Manually selecting viewpoints or using commonly available flight planners like circular path
for large-scale 3D reconstruction using drones often results in incomplete 3D models …

In this work, we present DiPPeR, a novel and fast 2D path planning framework for
quadrupedal locomotion, leveraging diffusion-driven techniques. Our contributions include a …

Data-driven methods such as reinforcement and imitation learning have achieved
remarkable success in robot autonomy. However, their data-centric nature still hinders them …

Effectively utilizing the vast amounts of ego-centric navigation data that is freely available on
the internet can advance generalized intelligent systems, ie, to robustly scale across …

We present a neural network collision checking heuristic, ClearanceNet, and a planning
algorithm, CN-RRT. ClearanceNet learns to predict separation distance (minimum distance …