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Found 6 papers, 1 papers with code
Learning to navigate efficiently and precisely in real environments
In the context of autonomous navigation of terrestrial robots, the creation of realistic models for agent dynamics and sensing is a widespread habit in the robotics literature and in commercial applications, where they are used for model based control and/or for localization and mapping.
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.
End-to-End (Instance)-Image Goal Navigation through Correspondence as an Emergent Phenomenon
The main challenge lies in learning compact representations generalizable to unseen environments and in learning high-capacity perception modules capable of reasoning on high-dimensional input.
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.
SIM2REALVIZ: Visualizing the Sim2Real Gap in Robot Ego-Pose Estimation
The Robotics community has started to heavily rely on increasingly realistic 3D simulators for large-scale training of robots on massive amounts of data.
