20,493 research outputs found
A Novel Reinforcement-Based Paradigm for Children to Teach the Humanoid Kaspar Robot
© The Author(s) 2019. This is the final published version of an article published in Psychological Research, licensed under a Creative Commons Attri-bution 4.0 International License. Available online at: https://doi.org/10.1007/s12369-019-00607-xThis paper presents a contribution to the active field of robotics research with the aim of supporting the development of social and collaborative skills of children with Autism Spectrum Disorders (ASD). We present a novel experiment where the classical roles are reversed: in this scenario the children are the teachers providing positive or negative reinforcement to the Kaspar robot in order for the robot to learn arbitrary associations between different toy names and the locations where they are positioned. The objective of this work is to develop games which help children with ASD develop collaborative skills and also provide them tangible example to understand that sometimes learning requires several repetitions. To facilitate this game we developed a reinforcement learning algorithm enabling Kaspar to verbally convey its level of uncertainty during the learning process, so as to better inform the children interacting with Kaspar the reasons behind the successes and failures made by the robot. Overall, 30 Typically Developing (TD) children aged between 7 and 8 (19 girls, 11 boys) and 6 children with ASD performed 22 sessions (16 for TD; 6 for ASD) of the experiment in groups, and managed to teach Kaspar all associations in 2 to 7 trials. During the course of study Kaspar only made rare unexpected associations (2 perseverative errors and 1 win-shift, within a total of 272 trials), primarily due to exploratory choices, and eventually reached minimal uncertainty. Thus the robot's behavior was clear and consistent for the children, who all expressed enthusiasm in the experiment.Peer reviewe
Learning Task Priorities from Demonstrations
Bimanual operations in humanoids offer the possibility to carry out more than
one manipulation task at the same time, which in turn introduces the problem of
task prioritization. We address this problem from a learning from demonstration
perspective, by extending the Task-Parameterized Gaussian Mixture Model
(TP-GMM) to Jacobian and null space structures. The proposed approach is tested
on bimanual skills but can be applied in any scenario where the prioritization
between potentially conflicting tasks needs to be learned. We evaluate the
proposed framework in: two different tasks with humanoids requiring the
learning of priorities and a loco-manipulation scenario, showing that the
approach can be exploited to learn the prioritization of multiple tasks in
parallel.Comment: Accepted for publication at the IEEE Transactions on Robotic
A Framework for Interactive Teaching of Virtual Borders to Mobile Robots
The increasing number of robots in home environments leads to an emerging
coexistence between humans and robots. Robots undertake common tasks and
support the residents in their everyday life. People appreciate the presence of
robots in their environment as long as they keep the control over them. One
important aspect is the control of a robot's workspace. Therefore, we introduce
virtual borders to precisely and flexibly define the workspace of mobile
robots. First, we propose a novel framework that allows a person to
interactively restrict a mobile robot's workspace. To show the validity of this
framework, a concrete implementation based on visual markers is implemented.
Afterwards, the mobile robot is capable of performing its tasks while
respecting the new virtual borders. The approach is accurate, flexible and less
time consuming than explicit robot programming. Hence, even non-experts are
able to teach virtual borders to their robots which is especially interesting
in domains like vacuuming or service robots in home environments.Comment: 7 pages, 6 figure
On the Integration of Adaptive and Interactive Robotic Smart Spaces
© 2015 Mauro Dragone et al.. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. (CC BY-NC-ND 3.0)Enabling robots to seamlessly operate as part of smart spaces is an important and extended challenge for robotics R&D and a key enabler for a range of advanced robotic applications, such as AmbientAssisted Living (AAL) and home automation. The integration of these technologies is currently being pursued from two largely distinct view-points: On the one hand, people-centred initiatives focus on improving the userâs acceptance by tackling human-robot interaction (HRI) issues, often adopting a social robotic approach, and by giving to the designer and - in a limited degree â to the final user(s), control on personalization and product customisation features. On the other hand, technologically-driven initiatives are building impersonal but intelligent systems that are able to pro-actively and autonomously adapt their operations to fit changing requirements and evolving usersâ needs,but which largely ignore and do not leverage human-robot interaction and may thus lead to poor user experience and user acceptance. In order to inform the development of a new generation of smart robotic spaces, this paper analyses and compares different research strands with a view to proposing possible integrated solutions with both advanced HRI and online adaptation capabilities.Peer reviewe
Semantic Robot Programming for Goal-Directed Manipulation in Cluttered Scenes
We present the Semantic Robot Programming (SRP) paradigm as a convergence of
robot programming by demonstration and semantic mapping. In SRP, a user can
directly program a robot manipulator by demonstrating a snapshot of their
intended goal scene in workspace. The robot then parses this goal as a scene
graph comprised of object poses and inter-object relations, assuming known
object geometries. Task and motion planning is then used to realize the user's
goal from an arbitrary initial scene configuration. Even when faced with
different initial scene configurations, SRP enables the robot to seamlessly
adapt to reach the user's demonstrated goal. For scene perception, we propose
the Discriminatively-Informed Generative Estimation of Scenes and Transforms
(DIGEST) method to infer the initial and goal states of the world from RGBD
images. The efficacy of SRP with DIGEST perception is demonstrated for the task
of tray-setting with a Michigan Progress Fetch robot. Scene perception and task
execution are evaluated with a public household occlusion dataset and our
cluttered scene dataset.Comment: published in ICRA 201
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