Robot methods for human-robot spatial language interaction

Title from PDF of title page (University of Missouri--Columbia, viewed on September 10, 2013).The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file.Thesis advisor: Dr. Marjorie SkubicIncludes bibliographical references.M.S. University of Missouri--Columbia 2013.Dissertations, Academic -- University of Missouri--Columbia -- Electrical engineering."May 2013"This thesis talks about a work to design a robot with some to interact with human by spatial language. The robot is a differential drive robot with Kinect camera. The thesis proposes the perception methods which include furniture recognition, furniture orientation detection and robot reposition for recognition performance improvement. The perception uses RGB-Depth image and extracts furniture samples and recognize them by using linguistic model and probability model. A novel method is designed for furniture position and orientation detection. The thesis also shows a method of using robot reposition to improve the recognition performance. The thesis also talks on human robot interaction. It gives a model which can convert human natural spatial language to robot navigation instructions. Several experiments in both physical world and simulation are run to test the efficiency of these algorithms

Spatial language driven robot

This dissertation investigates the methods to enable a robot to interact with human using spatial language. A prototype system of human-robot interaction using spatial language running on an autonomous robot is proposed in the dissertation. The system includes two complementary works. One is to control the robot by human natural spatial language to find the target object to fetch it. Another work is to generate a natural spatial language description to describe a target object in the robot working environment. The first task is called spatial language grounding and the second work is named as spatial language generation. The spatial language grounding and generation are both end-to-end process which means the system will determine the output only by the natural language command from a human during the interaction and the raw perception data collected from the environment. Furniture recognizers are designed for the robot to detect the environment during the tasks. A hierarchy system is designed to translate the human spatial language to the symbolic grounding model and then to the robot actions. To reduce the ambiguity in the interaction, a human demonstration system is designed to collect the spatial concept of the human user for building the robot behavior policies under different grounding models. A language generation system trained by real human spatial language corpus is proposed to automatically edit spatial descriptions of the location of a target object. All the modules in the system are evaluated in the physical environment, and a 3D robot simulator developed on ROS and GAZEBO.Includes biblographical reference

Lactobacillus plantarum KLDS1.0318 Ameliorates Impaired Intestinal Immunity and Metabolic Disorders in Cyclophosphamide-Treated Mice

Cyclophosphamide (CTX), a clinically important antineoplastic drug, also leads to some side effects such as nausea, vomiting and diarrhea in the consumer. In this study, Lactobacillus plantarum (L. plantarum) KLDS1.0318 preserved in our laboratory was orally administered to CTX-treated mice to explore its potential effects to attenuate the toxic effects of CTX-induced by modulating intestinal immune response, promoting intestinal integrity and improving metabolic profile. BALB/c mice were randomly divided into six groups including normal control group (NC; non-CTX with sterile saline), model control group (MC; CTX-treated with sterile saline), CTX-treated with L. plantarum KLDS1.0318 (10 mL/kg) groups with three different doses (KLDS1.0318-L, 5 × 107 CFU/mL; KLDS1.0318-M, 5 × 108 CFU/mL; KLDS1.0318-H, 5 × 109 CFU/mL), and CTX-treated with levamisole hydrochloride (40 mg/kg) as a positive control (PC) group. After receiving the bacterium for 20 days, samples of small intestine and colonic contents were collected for different analyses. The results revealed that the levels of cytokines secreted by Th1 cells (IL-2, IFN-γ, and TNF-α) and Th2 cells (IL-4, IL-6, and IL-10) in probiotic treatment groups were significantly higher than those in the MC group. Histopathological results showed that L. plantarum KLDS1.0318 favorably recovered CTX-induced abnormal intestinal morphology by improving the villus height and crypt depth as well as quantity of goblet cells and mucins production. Compared to CTX alone-treated group, the production of short-chain fatty acids (SCFAs) were significantly increased and the levels of pH and ammonia were decreased significantly with high dose L. plantarum KLDS1.0318 supplementation. Compared with mice in CTX alone-treated group, mice in three groups of KLDS1.0318 had increased Bifidobacterium and Lactobacillus and decreased Escherichia and Enterococcus in their cecal content. The present findings suggested that L. plantarum KLDS1.0318 could be of significant advantage to mitigate the harmful effects of CTX and improve the intestinal health in mice

Spatial Language Driven Robot

This dissertation investigates the methods to enable a robot to interact with human using spatial language. A prototype system of human-robot interaction using spatial language running on an autonomous robot is proposed in the dissertation. The system includes two complementary works. One is to control the robot by human natural spatial language to find the target object to fetch it. Another work is to generate a natural spatial language description to describe a target object in the robot working environment. The first task is called spatial language grounding and the second work is named as spatial language generation. The spatial language grounding and generation are both end-to-end process which means the system will determine the output only by the natural language command from a human during the interaction and the raw perception data collected from the environment. Furniture recognizers are designed for the robot to detect the environment during the tasks. A hierarchy system is designed to translate the human spatial language to the symbolic grounding model and then to the robot actions. To reduce the ambiguity in the interaction, a human demonstration system is designed to collect the spatial concept of the human user for building the robot behavior policies under different grounding models. A language generation system trained by real human spatial language corpus is proposed to automatically edit spatial descriptions of the location of a target object. All the modules in the system are evaluated in the physical environment, and a 3D robot simulator developed on ROS and GAZEBO

Evaluation of Flood Prediction Capability of the WRF-Hydro Model Based on Multiple Forcing Scenarios

The Weather Research and Forecasting (WRF)-Hydro model as a physical-based, fully-distributed, multi-parameterization modeling system easy to couple with numerical weather prediction model, has potential for operational flood forecasting in the small and medium catchments (SMCs). However, this model requires many input forcings, which makes it difficult to use it for the SMCs without adequate observed forcings. The Global Land Data Assimilation System (GLDAS), the WRF outputs and the ideal forcings generated by the WRF-Hydro model can provide all forcings required in the model for these SMCs. In this study, seven forcing scenarios were designed based on the products of GLDAS, WRF and ideal forcings, as well as the observed and merged rainfalls to assess the performance of the WRF-Hydro model for flood simulation. The model was applied to the Chenhe catchment, a typical SMC located in the Midwestern China. The flood prediction capability of the WRF-Hydro model was also compared to that of widely used Xinanjiang model. The results show that the three forcing scenarios, including the GLDAS forcings with observed rainfall, the WRF forcings with observed rainfall and GLDAS forcings with GLDAS-merged rainfall, are optimal input forcings for the WRF-Hydro model. Their mean root mean square errors (RMSE) are 0.18, 0.18 and 0.17 mm/h, respectively. The performance of the WRF-Hydro model driven by these three scenarios is generally comparable to that of the Xinanjiang model (RMSE = 0.17 mm/h)

Insect tissue-specific vitellogenin facilitates transmission of plant virus

<div><p>Insect vitellogenin (Vg) has been considered to be synthesized in the fat body. Here, we found that abundant Vg protein is synthesized in <i>Laodelphax striatellus</i> hemocytes as well. We also determined that only the hemocyte-produced Vg binds to Rice stripe virus (RSV) <i>in vivo</i>. Examination of the subunit composition of <i>L</i>. <i>striatellus</i> Vg (LsVg) revealed that LsVg was processed differently after its expression in different tissues. The LsVg subunit able to bind to RSV exist stably only in hemocytes, while fat body-produced LsVg lacks the RSV-interacting subunit. Nymph and male <i>L</i>. <i>striatellus</i> individuals also synthesize Vg but only in hemocytes, and the proteins co-localize with RSV. We observed that knockdown of <i>LsVg</i> transcripts by RNA interference decreased the RSV titer in the hemolymph, and thus interfered with systemic virus infection. Our results reveal the sex-independent expression and tissue-specific processing of LsVg and also unprecedentedly connect the function of this protein in mediating virus transmission to its particular molecular forms existing in tissues previously known as non-Vg producing.</p></div

Subunit composition of LsVn.

<p><b>A.</b> SDS-PAGE (10%) of purified LsVn. M is the molecular weight marker (kDa). The identified LsVn subunits are indicated by the arrows on the right. <b>B.</b> Mapping of vitellogenin-derived peptides identified by mass spectrometry onto the LsVg primary sequence. Peptides identified from SDS-PAGE bands are indicated by color: 178 kDa (shaded), 111 kDa (green), 67 kDa (blue) and 42 kDa (red). Pairs of arrows mark the span of LsVg or LsVn subunits. Shaded tetra-residues in bold font are the cleavage sites. Underlined sequences indicate synthetic peptides used for the production of subunit-specific antibodies. The predicted signal peptide sequence at the N-terminus is shown in bold. <b>C.</b> Verification of the composition of the LsVn subunit by western blot analysis. Purified LsVn was fractionated by SDS-PAGE (10%) and probed with the subunit-specific antibodies. Identified LsVn subunits are indicated by the arrows on the right. M, the molecular weight marker (kDa).</p

Tissue-specific processing of LsVg in female <i>L</i>. <i>striatellus</i>.

<p><b>A.</b> Confocal microscopy to reveal the distribution of different LsVg protein regions in the fat body or hemocytes. The LsVg N-terminus (recognized by antibody Ab42K) was present in both tissues, whereas the middle region (recognized by Ab67K2) and the C-terminus (recognized by Ab111K) existed only in hemocytes. LsVg probed with the LsVn subunit-specific antibody was stained with Alexa Fluor 568 (shown in red). RSV was stained with Alexa Fluor 488 (shown in green). Nucleoli were stained with TO-PRO-3 (shown in blue). Images were examined using a Leica TCS SP8 confocal microscope. The scale bar represents 20 μm. <b>B.</b> Western blots to determine the molecular weights and subunit distribution of proteins in the fat body (FB) or hemolymph (HL). Extracted hemolymph or fat-body proteins were fractionated by SDS-PAGE (10%) and probed with the subunit-specific antibodies Ab42K, Ab67K2 and Ab111K. M is the molecular weight marker (kDa). Identified LsVg subunits are indicated by the arrows on the right. <b>C.</b> Confocal microscopic images showing co-localization of the N-terminal small (Small) and C-terminal large (Large) subunits of LsVg. The large subunit was probed with antibody Ab111Km and stained with Alexa Fluor 488 (shown in green). The small subunit was probed with antibody Ab42K and stained with Alexa Fluor 568 (shown in red). Images were examined using a Leica TCS SP8 confocal microscope. The scale bar represents 20 μm. <b>D.</b> The mRNA abundance of LsVn subunits. The mRNA copy numbers were determined by SYBR Green-based <i>q</i>PCR. Each dot, square or triangle represents one fat-body sample collected from one female SBPH. <i>NS</i>, not significant. <b>E.</b> Western blots showing the influence of subunit-specific gene silencing on expression levels of multiple subunits. RNAi with <i>ds</i>RNA specific to either the N-terminal small (Small) or C-terminal large (Large) subunit dramatically decreased expression levels of both subunits. RNAi with <i>dsGFP</i> was used as a negative control and did not influence the expression of LsVg. Protein levels were detected with antibodies Ab67K2 or Ab42K. M is the molecular weight marker (kDa). Positions of the LsVg subunits (Small and Large) are indicated by the arrows on the right.</p