Robot Learning with Task-Parameterized Generative Models
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We consider the problem of performing rapid training of a terrain classier in the context of a collaborative robotic search and rescue system. Our system uses a vision-based flying robot to guide a ground robot through unknown terrain to a goal location by ...
Task-parameterized models provide a representation of movement/behavior that can adapt to a set of task parameters describing the current situation encountered by the robot, such as location of objects or landmarks in its workspace. This paper gives an ove ...
The goal of this special issue is to document and highlight recent progress in robot learning for human–robot collaboration (HRC), covering a diversity of articles that reflect the state-of-the-art in the field. Following an open call for papers, we receiv ...
Recently, the notion that the brain is fundamentally a prediction machine has gained traction within the cognitive science community. Consequently, the ability to learn accurate predictors from experience is crucial to creating intelligent robots. However, ...
Social robots are being used to create better educational scenarios, thereby fostering children's learning. In the work presented here, we describe an autonomous social robot that was designed to enhance children's handwriting skills. Exploiting the benefi ...
Lightweight, autonomous drones are soon expected to be used in a wide variety of tasks such as aerial surveillance, delivery, or monitoring of existing architectures. A large body of literature in robotic perception and control exists. Existing methods are ...
The way human beings engage with material things in our environment is experiencing rapid modification. Human and non-human, natural and artificial creatures are on the verge of building unprecedented relations of sociability. This paper takes this process ...
Human-robot collaboration seeks to have humans and robots closely interacting in everyday situations. For some tasks, physical contact between the user and the robot may occur, originating significant challenges at safety, cognition, perception and control ...
Robot Learning from Demonstration (RLfD) has been identified as a key element for making robots useful in daily lives. A wide range of techniques has been proposed for deriving a task model from a set of demonstrations of the task. Most previous works use ...
This chapter surveys the main approaches developed to date to endow robots with the ability to learn from human guidance. The field is best known as robot programming by demonstration, robot learning from/by demonstration, apprenticeship learning and imita ...