84 papers with code • 0 benchmarks • 4 datasets
Autonomous navigation is the task of autonomously navigating a vehicle or robot to or around a location without human guidance.
These leaderboards are used to track progress in Autonomous Navigation
Accurate detection of objects in 3D point clouds is a central problem in many applications, such as autonomous navigation, housekeeping robots, and augmented/virtual reality.
We leverage this scaling to train an agent for 2. 5 Billion steps of experience (the equivalent of 80 years of human experience) -- over 6 months of GPU-time training in under 3 days of wall-clock time with 64 GPUs.
DeepTraffic: Crowdsourced Hyperparameter Tuning of Deep Reinforcement Learning Systems for Multi-Agent Dense Traffic Navigation
We present a traffic simulation named DeepTraffic where the planning systems for a subset of the vehicles are handled by a neural network as part of a model-free, off-policy reinforcement learning process.
To tackle this issue, in this paper we propose a novel architecture capable to quickly infer an accurate depth map on a CPU, even of an embedded system, using a pyramid of features extracted from a single input image.
Learning socially-aware motion representations is at the core of recent advances in multi-agent problems, such as human motion forecasting and robot navigation in crowds.
We present an interactive navigation environment that uses Google StreetView for its photographic content and worldwide coverage, and demonstrate that our learning method allows agents to learn to navigate multiple cities and to traverse to target destinations that may be kilometres away.
As part of our general methodology we discuss the software mapping techniques that enable the state-of-the-art deep convolutional neural network presented in  to be fully executed on-board within a strict 6 fps real-time constraint with no compromise in terms of flight results, while all processing is done with only 64 mW on average.
Obstacle detection by semantic segmentation shows a great promise for autonomous navigation in unmanned surface vehicles (USV).
In this work, we present Predicted Endpoint Conditioned Network (PECNet) for flexible human trajectory prediction.
For robotic vehicles to navigate safely and efficiently in pedestrian-rich environments, it is important to model subtle human behaviors and navigation rules (e. g., passing on the right).