3,068 research outputs found
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
Learning Task Specifications from Demonstrations
Real world applications often naturally decompose into several sub-tasks. In
many settings (e.g., robotics) demonstrations provide a natural way to specify
the sub-tasks. However, most methods for learning from demonstrations either do
not provide guarantees that the artifacts learned for the sub-tasks can be
safely recombined or limit the types of composition available. Motivated by
this deficit, we consider the problem of inferring Boolean non-Markovian
rewards (also known as logical trace properties or specifications) from
demonstrations provided by an agent operating in an uncertain, stochastic
environment. Crucially, specifications admit well-defined composition rules
that are typically easy to interpret. In this paper, we formulate the
specification inference task as a maximum a posteriori (MAP) probability
inference problem, apply the principle of maximum entropy to derive an analytic
demonstration likelihood model and give an efficient approach to search for the
most likely specification in a large candidate pool of specifications. In our
experiments, we demonstrate how learning specifications can help avoid common
problems that often arise due to ad-hoc reward composition.Comment: NIPS 201
Learning Articulated Motions From Visual Demonstration
Many functional elements of human homes and workplaces consist of rigid
components which are connected through one or more sliding or rotating
linkages. Examples include doors and drawers of cabinets and appliances;
laptops; and swivel office chairs. A robotic mobile manipulator would benefit
from the ability to acquire kinematic models of such objects from observation.
This paper describes a method by which a robot can acquire an object model by
capturing depth imagery of the object as a human moves it through its range of
motion. We envision that in future, a machine newly introduced to an
environment could be shown by its human user the articulated objects particular
to that environment, inferring from these "visual demonstrations" enough
information to actuate each object independently of the user.
Our method employs sparse (markerless) feature tracking, motion segmentation,
component pose estimation, and articulation learning; it does not require prior
object models. Using the method, a robot can observe an object being exercised,
infer a kinematic model incorporating rigid, prismatic and revolute joints,
then use the model to predict the object's motion from a novel vantage point.
We evaluate the method's performance, and compare it to that of a previously
published technique, for a variety of household objects.Comment: Published in Robotics: Science and Systems X, Berkeley, CA. ISBN:
978-0-9923747-0-
Behavior-Based Early Language Development on a Humanoid Robot
We are exploring the idea that early language acquisition could be better modelled on an artifcial creature by considering the pragmatic aspect of natural language and of its development in human infants. We have implemented a system of vocal behaviors on Kismet in which "words" or concepts are behaviors in a competitive hierarchy. This paper reports on the framework, the vocal system's architecture and algorithms, and some preliminary results from vocal label learning and concept formation
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