Route planning for navigation under partial observability plays a crucial role in modern
robotics and autonomous driving. Existing route planning approaches can be categorized …

We train embodied neural networks to plan and navigate unseen complex 3D environments,
emphasising real-world deployment. Rather than requiring prior knowledge of the agent or …

To help bridge the gap between internet vision-style problems and the goal of vision for
embodied perception we instantiate a large-scale navigation task--Embodied Question …

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 …

Real-time path planning in outdoor environments still challenges modern robotic systems
due to differences in terrain traversability, diverse obstacles, and the necessity for fast …

State-of-the-art navigation methods leverage a spatial memory to generalize to new
environments, but their occupancy maps are limited to capturing the geometric structures …

We present a reward-predictive, model-based learning method featuring trajectory-
constrained visual attention for use in mapless, local visual navigation tasks. Our method …

Reinforcement Learning (RL), among other learning-based methods, represents powerful
tools to solve complex robotic tasks (eg, actuation, manipulation, navigation, etc.), with the …

Learning effective visuomotor policies for robots purely from data is challenging, but also
appealing since a learning-based system should not require manual tuning or calibration. In …

Traversability prediction is a fundamental perception capability for autonomous navigation.
The diversity of data in different domains imposes significant gaps to the prediction …