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Found 6 papers, 0 papers with code
Multi-Object Navigation in real environments using hybrid policies
More recently, beyond waypoint planning, problems involving significant components of (visual) high-level reasoning have been explored in simulated environments, mostly addressed with large-scale machine learning, in particular RL, offline-RL or imitation learning.
Learning with a Mole: Transferable latent spatial representations for navigation without reconstruction
Agents navigating in 3D environments require some form of memory, which should hold a compact and actionable representation of the history of observations useful for decision taking and planning.
An in-depth experimental study of sensor usage and visual reasoning of robots navigating in real environments
In this work we present an in-depth study of the performance and reasoning capacities of real physical agents, trained in simulation and deployed to two different physical environments.
Universal Domain Adaptation in Ordinal Regression
We address the problem of universal domain adaptation (UDA) in ordinal regression (OR), which attempts to solve classification problems in which labels are not independent, but follow a natural order.
Adversarial Transfer of Pose Estimation Regression
We address the problem of camera pose estimation in visual localization.
Self-Supervised Attention Learning for Depth and Ego-motion Estimation
We address the problem of depth and ego-motion estimation from image sequences.
