Learning strategies and representations for intuitive robot learning from demonstration
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In the last years there has been an increasing interest on using human feedback during robot operation to incorporate non-expert human expertise while learning complex tasks. Most work has considered reinforcement learning frameworks were human feedback, p ...
The fast technological evolution and dissemination of multimodal sensors and compliant actuators bring a new human-centric perspective to robotics. The variety of human-robot interactions that stem from these new capabilities unveil compelling challenges f ...
Technology is playing an increasing role in our society. Therefore it becomes important to educate the general public, and young generations in particular, about the most common technologies. In this context, robots are excellent education tools, for many ...
This article reports on the results of a user study investigating the satisfaction of naïve users conducting two learning by demonstration tasks with the HOAP-3 robot. The main goal of this study was to gain insights on how to ensure a successful as well a ...
Automatically synthesizing behaviors for robots with articulated bodies poses a number of challenges beyond those encountered when generating behaviors for simpler agents. One such challenge is how to optimize a controller that can orchestrate dynamic moti ...
The fast technological evolution and dissemination of multimodal sensors and compliant actuators bring a new human-centric perspective to robotics. The variety of human-robot interactions that stem from these new capabilities unveil compelling challenges f ...
Interactive learning deals with the problem of learning and solving tasks using human instructions. It is common in human-robot interaction, tutoring systems, and in human-computer interfaces such as brain-computer ones. In most cases, learning these tasks ...
While there is a general consensus that autonomous robots should be able to learn continuously over time, the learning process is traditionally envisioned for each specific robot situated in a given environment. This does not consider the fact that robots ...
This paper presents a novel semi-autonomous navigation strategy designed for low throughput interfaces. A mobile robot (e.g. intelligent wheelchair) proposes the most probable action, as analyzed from the environment, to a human user who can either accept ...
We consider the problem of learning robust models of robot motion through demonstration. An approach based on Hidden Markov Model (HMM) and Gaussian Mixture Regression (GMR) is proposed to extract redundancies across multiple demonstrations, and build a ti ...
