12,076 research outputs found
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
Recovering from External Disturbances in Online Manipulation through State-Dependent Revertive Recovery Policies
Robots are increasingly entering uncertain and unstructured environments.
Within these, robots are bound to face unexpected external disturbances like
accidental human or tool collisions. Robots must develop the capacity to
respond to unexpected events. That is not only identifying the sudden anomaly,
but also deciding how to handle it. In this work, we contribute a recovery
policy that allows a robot to recovery from various anomalous scenarios across
different tasks and conditions in a consistent and robust fashion. The system
organizes tasks as a sequence of nodes composed of internal modules such as
motion generation and introspection. When an introspection module flags an
anomaly, the recovery strategy is triggered and reverts the task execution by
selecting a target node as a function of a state dependency chart. The new
skill allows the robot to overcome the effects of the external disturbance and
conclude the task. Our system recovers from accidental human and tool
collisions in a number of tasks. Of particular importance is the fact that we
test the robustness of the recovery system by triggering anomalies at each node
in the task graph showing robust recovery everywhere in the task. We also
trigger multiple and repeated anomalies at each of the nodes of the task
showing that the recovery system can consistently recover anywhere in the
presence of strong and pervasive anomalous conditions. Robust recovery systems
will be key enablers for long-term autonomy in robot systems. Supplemental info
including code, data, graphs, and result analysis can be found at [1].Comment: 8 pages, 8 figures, 1 tabl
Statistical interaction modeling of bovine herd behaviors
While there has been interest in modeling the group behavior of herds or flocks, much of this work has focused on simulating their collective spatial motion patterns which have not accounted for individuality in the herd and instead assume a homogenized role for all members or sub-groups of the herd. Animal behavior experts have noted that domestic animals exhibit behaviors that are indicative of social hierarchy: leader/follower type behaviors are present as well as dominance and subordination, aggression and rank order, and specific social affiliations may also exist. Both wild and domestic cattle are social species, and group behaviors are likely to be influenced by the expression of specific social interactions. In this paper, Global Positioning System coordinate fixes gathered from a herd of beef cows tracked in open fields over several days at a time are utilized to learn a model that focuses on the interactions within the herd as well as its overall movement. Using these data in this way explores the validity of existing group behavior models against actual herding behaviors. Domain knowledge, location geography and human observations, are utilized to explain the causes of these deviations from this idealized behavior
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Uncertainty quantification and its properties for hidden Markov models with application to condition based maintenance
Condition-based maintenance (CBM) can be viewed as a transformation of data gathered from a piece of equipment into information about its condition, and further into decisions on what to do with the equipment. Hidden Markov model (HMM) is a useful framework to probabilistically model the condition of complex engineering systems with partial observability of the underlying states. Condition monitoring and prediction of such type of system requires accurate knowledge of HMM that describes the degradation of such a system with data collected from the sensors mounted on it, as well as understanding of the uncertainty of the HMMs identified from the available data. To that end, this thesis proposes a novel HMM estimation scheme based on the principles of Bayes theorem. The newly proposed Bayesian estimation approach for estimating HMM parameters naturally yields information about model parametric uncertainties via posterior distributions of HMM parameters emanating from the estimation process. In addition, a novel condition monitoring scheme based on uncertain
HMMs of the degradation process is proposed and demonstrated on a large dataset obtained from a semiconductor manufacturing facility. Portion of the data was used to build operating mode specific HMMs of machine degradation via the newly proposed Bayesian estimation process, while the remainder of the data was used for monitoring of machine condition using the uncertain degradation HMMs yielded by Bayesian estimation. Comparison with a traditional signature-based statistical monitoring method showed that the newly proposed approach effectively utilizes the fact that its parameters are uncertain themselves, leading to orders of magnitude fewer false alarms. This methodology is further extended to address the practical issue that maintenance interventions are usually imperfect. We propose both a novel non-ergodic and non-homogeneous HMM that assumes imperfect maintenances and a novel process monitoring method capable of monitoring the hidden states considering model uncertainty. Significant improvement in both the log-likelihood of estimated HMM parameters and monitoring performance were observed, compared to those obtained using degradation HMMs that always assumed perfect maintenance.
Finally, behavior of the posterior distribution of parameters of unidirectional non- ergodic HMMs modeling in this thesis for degradation was theoretically analyzed in terms of their evolution as more data become available in the estimation process. The convergence problem is formulated as a Bernstein-von Mises theorem (BvMT), and under certain regularity conditions, the sequence of posterior distributions is proven to converge to a Gaussian distribution with variance matrix being the inverse of the Fisher information matrix. An example of a unidirectional HMM is presented for which the regularity conditions are verified, and illustrations of expected theoretical results are given using simulation. The understanding of such convergence of posterior distributions
enables one to determine when Bayesian estimation of degradation HMMs is justified and converges toward true model parameters, as well as how much data one then needs to achieve desired accuracy of the resulting model. Understanding of these issues is of utmost important if HMMs are to be used for degradation modeling and monitoring.Operations Research and Industrial Engineerin
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
Practical issues for the implementation of survivability and recovery techniques in optical networks
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