Interactively Picking Real-World Objects with Unconstrained Spoken Language Instructions

Comprehension of spoken natural language is an essential component for robots to communicate with human effectively. However, handling unconstrained spoken instructions is challenging due to (1) complex structures including a wide variety of expressions used in spoken language and (2) inherent ambiguity in interpretation of human instructions. In this paper, we propose the first comprehensive system that can handle unconstrained spoken language and is able to effectively resolve ambiguity in spoken instructions. Specifically, we integrate deep-learning-based object detection together with natural language processing technologies to handle unconstrained spoken instructions, and propose a method for robots to resolve instruction ambiguity through dialogue. Through our experiments on both a simulated environment as well as a physical industrial robot arm, we demonstrate the ability of our system to understand natural instructions from human operators effectively, and how higher success rates of the object picking task can be achieved through an interactive clarification process.Comment: 9 pages. International Conference on Robotics and Automation (ICRA) 2018. Accompanying videos are available at the following links: https://youtu.be/_Uyv1XIUqhk (the system submitted to ICRA-2018) and http://youtu.be/DGJazkyw0Ws (with improvements after ICRA-2018 submission

Fast Newton-CG Method for Batch Learning of Conditional Random Fields

We propose a fast batch learning method for linear-chain Conditional Random Fields (CRFs) based on Newton-CG methods. Newton-CG methods are a variant of Newton method for high-dimensional problems. They only require the Hessian-vector products instead of the full Hessian matrices. To speed up Newton-CG methods for the CRF learning, we derive a novel dynamic programming procedure for the Hessian-vector products of the CRF objective function. The proposed procedure can reuse the byproducts of the time-consuming gradient computation for the Hessian-vector products to drastically reduce the total computation time of the Newton-CG methods. In experiments with tasks in natural language processing, the proposed method outperforms a conventional quasi-Newton method. Remarkably, the proposed method is competitive with online learning algorithms that are fast but unstable

Measurements of ttˉt\bar{t} differential cross-sections of highly boosted top quarks decaying to all-hadronic final states in pppp collisions at s=13 \sqrt{s}=13\, TeV using the ATLAS detector

Measurements are made of differential cross-sections of highly boosted pair-produced top quarks as a function of top-quark and $t\bar{t}$ system kinematic observables using proton--proton collisions at a center-of-mass energy of $\sqrt{s} = 13$ TeV. The data set corresponds to an integrated luminosity of $36.1$ fb$^{-1}$, recorded in 2015 and 2016 with the ATLAS detector at the CERN Large Hadron Collider. Events with two large-radius jets in the final state, one with transverse momentum $p_{\rm T} > 500$ GeV and a second with $p_{\rm T}>350$ GeV, are used for the measurement. The top-quark candidates are separated from the multijet background using jet substructure information and association with a $b$-tagged jet. The measured spectra are corrected for detector effects to a particle-level fiducial phase space and a parton-level limited phase space, and are compared to several Monte Carlo simulations by means of calculated $\chi^2$ values. The cross-section for $t\bar{t}$ production in the fiducial phase-space region is $292 \pm 7 \ \rm{(stat)} \pm 76 \rm{(syst)}$ fb, to be compared to the theoretical prediction of $384 \pm 36$ fb

Search for Scalar Diphoton Resonances in the Mass Range 65−60065-600 GeV with the ATLAS Detector in pppp Collision Data at s\sqrt{s} = 8 TeVTeV

A search for scalar particles decaying via narrow resonances into two photons in the mass range 65–600 GeV is performed using $20.3\text{}\text{}{\mathrm{fb}}^{-1}$ of $\sqrt{s}=8\text{}\text{}\mathrm{TeV}$ $pp$ collision data collected with the ATLAS detector at the Large Hadron Collider. The recently discovered Higgs boson is treated as a background. No significant evidence for an additional signal is observed. The results are presented as limits at the 95% confidence level on the production cross section of a scalar boson times branching ratio into two photons, in a fiducial volume where the reconstruction efficiency is approximately independent of the event topology. The upper limits set extend over a considerably wider mass range than previous searches

Measurements of ttˉt\bar{t} differential cross-sections of highly boosted top quarks decaying to all-hadronic final states in pppp collisions at s=13 \sqrt{s}=13\, TeV using the ATLAS detector

Measurements are made of differential cross-sections of highly boosted pair-produced top quarks as a function of top-quark and $t\bar{t}$ system kinematic observables using proton--proton collisions at a center-of-mass energy of $\sqrt{s} = 13$ TeV. The data set corresponds to an integrated luminosity of $36.1$ fb$^{-1}$, recorded in 2015 and 2016 with the ATLAS detector at the CERN Large Hadron Collider. Events with two large-radius jets in the final state, one with transverse momentum $p_{\rm T} > 500$ GeV and a second with $p_{\rm T}>350$ GeV, are used for the measurement. The top-quark candidates are separated from the multijet background using jet substructure information and association with a $b$-tagged jet. The measured spectra are corrected for detector effects to a particle-level fiducial phase space and a parton-level limited phase space, and are compared to several Monte Carlo simulations by means of calculated $\chi^2$ values. The cross-section for $t\bar{t}$ production in the fiducial phase-space region is $292 \pm 7 \ \rm{(stat)} \pm 76 \rm{(syst)}$ fb, to be compared to the theoretical prediction of $384 \pm 36$ fb