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

Bayesian inference of phylogenetic networks from bi-allelic genetic markers.

Phylogenetic networks are rooted, directed, acyclic graphs that model reticulate evolutionary histories. Recently, statistical methods were devised for inferring such networks from either gene tree estimates or the sequence alignments of multiple unlinked loci. Bi-allelic markers, most notably single nucleotide polymorphisms (SNPs) and amplified fragment length polymorphisms (AFLPs), provide a powerful source of genome-wide data. In a recent paper, a method called SNAPP was introduced for statistical inference of species trees from unlinked bi-allelic markers. The generative process assumed by the method combined both a model of evolution for the bi-allelic markers, as well as the multispecies coalescent. A novel component of the method was a polynomial-time algorithm for exact computation of the likelihood of a fixed species tree via integration over all possible gene trees for a given marker. Here we report on a method for Bayesian inference of phylogenetic networks from bi-allelic markers. Our method significantly extends the algorithm for exact computation of phylogenetic network likelihood via integration over all possible gene trees. Unlike the case of species trees, the algorithm is no longer polynomial-time on all instances of phylogenetic networks. Furthermore, the method utilizes a reversible-jump MCMC technique to sample the posterior of phylogenetic networks given bi-allelic marker data. Our method has a very good performance in terms of accuracy and robustness as we demonstrate on simulated data, as well as a data set of multiple New Zealand species of the plant genus Ourisia (Plantaginaceae). We implemented the method in the publicly available, open-source PhyloNet software package

Illustrating the “growth” of lineages of a gene tree in a phylogenetic network.

<p>The histories of green and red alleles are shown as solid (green) lines and dashed (red) lines, respectively.</p

An illustration of the decompose-and-split operation.

<p>In this example, partial likelihood is decomposed into six vectors <b>F</b>₀ to <b>F</b>₅. An illustration of how <b>F</b>₄ is split in the four possible ways to trace branches <i>y</i> and <i>z</i> is shown, and every split is assigned a unique label.</p

The phylogenetic network used to investigate effect of multiple individuals.

<p>The branch lengths of the phylogenetic networks are measured in units of expected number of mutations per site. The inheritance probabilities are marked in blue.</p

Histograms of the branch lengths sampled by our method on the simulated data set corresponding to the phylogenetic network of Fig 3(A).

<p>Blue: 1,000 sites. Green: 10,000 sites. Black: 100,000 sites. Purple: 1,000,000 sites. The red dashed lines correspond to the true values.</p

The height of trees and networks sampled under different simulation settings and violations in the different assumptions.

<p>The red dashed lines correspond to the true values. In each panel at most one condition is violated. (a) Mean of 1.0 is used for the Poisson prior on the number of reticulations. (b) Mean of 3.0 is used for the Poisson prior on the number of reticulations. (c) Linked loci: 10 sites are generated per gene tree. (d) Linked loci: 100 sites are generated per gene tree. (e) Rate variation across lineages with 0.1 of invariable sites and 3.0 as shape of gamma rate heterogeneity. (f) Rate variation across lineages with 0.2 of invariable sites and 5.0 as shape of gamma rate heterogeneity. (g) Rate variation across markers with 0.1 of invariable sites and 3.0 as shape of gamma rate heterogeneity. (h) Rate variation across markers with 0.2 of invariable sites and 5.0 as shape of gamma rate heterogeneity.</p

The topological distance (pink) between sampled networks and true network, and the Robinson-Foulds distance (orange) between sampled trees and true backbone tree, under different simulation settings and violation in the different assumptions.

<p>In each panel at most one condition is violated. (a) Mean of 1.0 is used for the Poisson prior on the number of reticulations. (b) Mean of 3.0 is used for the Poisson prior on the number of reticulations. (c) Linked loci: 10 sites are generated per gene tree. (d) Linked loci: 100 sites are generated per gene tree. (e) Rate variation across lineages with 0.1 of invariable sites and 3.0 as shape of gamma rate heterogeneity. (f) Rate variation across lineages with 0.2 of invariable sites and 5.0 as shape of gamma rate heterogeneity. (g) Rate variation across markers with 0.1 of invariable sites and 3.0 as shape of gamma rate heterogeneity. (h) Rate variation across markers with 0.2 of invariable sites and 5.0 as shape of gamma rate heterogeneity.</p

A histogram of the inheritance probabilities sampled by our method on the simulated data set corresponding to the phylogenetic network of Fig 3(A).

<p>The five curves correspond to five independent runs. The red dashed line corresponds to the true value.</p

The MAP phylogenetic network for the subset with the hybrid <i>O.</i> × <i>prorepens</i> (<i>Meudt 203^a</i>, MPN 29774) and putative parents.

<p>The width of each tube is proportional to the population mutation rate of each branch, which is printed on each tube. The length of each tube is proportional to the length of the corresponding branch in units of expected number of mutations per site (scale shown). Blue arrows indicate the reticulation edges and their inheritance probabilities are printed in blue.</p