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Found 6 papers, 3 papers with code
Mobile Robot Manipulation using Pure Object Detection
We develop an end-to-end manipulation method based solely on detection and introduce Task-focused Few-shot Object Detection (TFOD) to learn new objects and settings.
Video Object Segmentation-based Visual Servo Control and Object Depth Estimation on a Mobile Robot Platform
To be useful in everyday environments, robots must be able to identify and locate unstructured, real-world objects.
Robotics
Learning Kinematic Descriptions using SPARE: Simulated and Physical ARticulated Extendable dataset
SPARE is an extendable open-source dataset providing equivalent simulated and physical instances of articulated objects (kinematic chains), providing the greater research community with a training and evaluation tool for methods generating kinematic descriptions of articulated objects.
A Critical Investigation of Deep Reinforcement Learning for Navigation
However, when trained and tested on different sets of maps, the algorithm fails to transfer the ability to gather and exploit map-information to unseen maps.
Predicting Future Lane Changes of Other Highway Vehicles using RNN-based Deep Models
To demonstrate our approach, we validate our model using authentic interstate highway driving to predict the future lane change maneuvers of other vehicles neighboring our autonomous vehicle.
Do Deep Reinforcement Learning Algorithms really Learn to Navigate?
As the title of the paper by Mirowski et al. (2016) suggests, one might assume that DRL-based algorithms are able to “learn to navigate” and are thus ready to replace classical mapping and path-planning algorithms, at least in simulated environments.
