9,890 research outputs found
Online Robot Introspection via Wrench-based Action Grammars
Robotic failure is all too common in unstructured robot tasks. Despite
well-designed controllers, robots often fail due to unexpected events. How do
robots measure unexpected events? Many do not. Most robots are driven by the
sense-plan act paradigm, however more recently robots are undergoing a
sense-plan-act-verify paradigm. In this work, we present a principled
methodology to bootstrap online robot introspection for contact tasks. In
effect, we are trying to enable the robot to answer the question: what did I
do? Is my behavior as expected or not? To this end, we analyze noisy wrench
data and postulate that the latter inherently contains patterns that can be
effectively represented by a vocabulary. The vocabulary is generated by
segmenting and encoding the data. When the wrench information represents a
sequence of sub-tasks, we can think of the vocabulary forming a sentence (set
of words with grammar rules) for a given sub-task; allowing the latter to be
uniquely represented. The grammar, which can also include unexpected events,
was classified in offline and online scenarios as well as for simulated and
real robot experiments. Multiclass Support Vector Machines (SVMs) were used
offline, while online probabilistic SVMs were are used to give temporal
confidence to the introspection result. The contribution of our work is the
presentation of a generalizable online semantic scheme that enables a robot to
understand its high-level state whether nominal or abnormal. It is shown to
work in offline and online scenarios for a particularly challenging contact
task: snap assemblies. We perform the snap assembly in one-arm simulated and
real one-arm experiments and a simulated two-arm experiment. This verification
mechanism can be used by high-level planners or reasoning systems to enable
intelligent failure recovery or determine the next most optima manipulation
skill to be used.Comment: arXiv admin note: substantial text overlap with arXiv:1609.0494
Robot Introspection with Bayesian Nonparametric Vector Autoregressive Hidden Markov Models
Robot introspection, as opposed to anomaly detection typical in process
monitoring, helps a robot understand what it is doing at all times. A robot
should be able to identify its actions not only when failure or novelty occurs,
but also as it executes any number of sub-tasks. As robots continue their quest
of functioning in unstructured environments, it is imperative they understand
what is it that they are actually doing to render them more robust. This work
investigates the modeling ability of Bayesian nonparametric techniques on
Markov Switching Process to learn complex dynamics typical in robot contact
tasks. We study whether the Markov switching process, together with Bayesian
priors can outperform the modeling ability of its counterparts: an HMM with
Bayesian priors and without. The work was tested in a snap assembly task
characterized by high elastic forces. The task consists of an insertion subtask
with very complex dynamics. Our approach showed a stronger ability to
generalize and was able to better model the subtask with complex dynamics in a
computationally efficient way. The modeling technique is also used to learn a
growing library of robot skills, one that when integrated with low-level
control allows for robot online decision making.Comment: final version submitted to humanoids 201
Fast, Robust, and Versatile Event Detection through HMM Belief State Gradient Measures
Event detection is a critical feature in data-driven systems as it assists
with the identification of nominal and anomalous behavior. Event detection is
increasingly relevant in robotics as robots operate with greater autonomy in
increasingly unstructured environments. In this work, we present an accurate,
robust, fast, and versatile measure for skill and anomaly identification. A
theoretical proof establishes the link between the derivative of the
log-likelihood of the HMM filtered belief state and the latest emission
probabilities. The key insight is the inverse relationship in which gradient
analysis is used for skill and anomaly identification. Our measure showed
better performance across all metrics than related state-of-the art works. The
result is broadly applicable to domains that use HMMs for event detection.Comment: 8 pages, 7 figures, double col, ieee conference forma
Importance and applications of robotic and autonomous systems (RAS) in railway maintenance sector: a review
Maintenance, which is critical for safe, reliable, quality, and cost-effective service, plays a dominant role in the railway industry. Therefore, this paper examines the importance and applications of Robotic and Autonomous Systems (RAS) in railway maintenance. More than 70 research publications, which are either in practice or under investigation describing RAS developments in the railway maintenance, are analysed. It has been found that the majority of RAS developed are for rolling-stock maintenance, followed by railway track maintenance. Further, it has been found that there is growing interest and demand for robotics and autonomous systems in the railway maintenance sector, which is largely due to the increased competition, rapid expansion and ever-increasing expense
On inferring intentions in shared tasks for industrial collaborative robots
Inferring human operators' actions in shared collaborative tasks, plays a crucial role in enhancing the cognitive capabilities of industrial robots. In all these incipient collaborative robotic applications, humans and robots not only should share space but also forces and the execution of a task. In this article, we present a robotic system which is able to identify different human's intentions and to adapt its behavior consequently, only by means of force data. In order to accomplish this aim, three major contributions are presented: (a) force-based operator's intent recognition, (b) force-based dataset of physical human-robot interaction and (c) validation of the whole system in a scenario inspired by a realistic industrial application. This work is an important step towards a more natural and user-friendly manner of physical human-robot interaction in scenarios where humans and robots collaborate in the accomplishment of a task.Peer ReviewedPostprint (published version
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