Cognitive Approach to Hierarchical Task Selection for Human-Robot Interaction in Dynamic Environments

In an efficient and flexible human-robot collaborative work environment, a robot team member must be able to recognize both explicit requests and implied actions from human users. Identifying "what to do" in such cases requires an agent to have the ability to construct associations between objects, their actions, and the effect of actions on the environment. In this regard, semantic memory is being introduced to understand the explicit cues and their relationships with available objects and required skills to make "tea" and "sandwich". We have extended our previous hierarchical robot control architecture to add the capability to execute the most appropriate task based on both feedback from the user and the environmental context. To validate this system, two types of skills were implemented in the hierarchical task tree: 1) Tea making skills and 2) Sandwich making skills. During the conversation between the robot and the human, the robot was able to determine the hidden context using ontology and began to act accordingly. For instance, if the person says "I am thirsty" or "It is cold outside" the robot will start to perform the tea-making skill. In contrast, if the person says, "I am hungry" or "I need something to eat", the robot will make the sandwich. A humanoid robot Baxter was used for this experiment. We tested three scenarios with objects at different positions on the table for each skill. We observed that in all cases, the robot used only objects that were relevant to the skill.Comment: To Appear In International Conference on Intelligent Robots and Systems (IROS), Detroit, MI, USA, Oct 202

Somatic Cell Cycle Regulation By Histone H3 Modifications: Action of OGT and Kinases

Abstract.-Histone H3 is amongst the most evolutionarily conserved proteins, and is located along with histone 2A, 2B and 4 in the core of the nucleosome. The N-terminal tails of the histone protrude the chromatin structure and become accessible to various enzymes for post translational modifications (PTMs). Phosphorylation of H3 has been found to have an impact on progression of the cell cycle, especially during mitosis. Another equally abundant PTM is the glycosylation at serine/threonine by O-GlcNAc (O-linked glycosylation) that occurs on the same or neighboring Ser or Thr residues, which also are accessible to kinases (Yin Yang sites). O-GlcNAc is added by O-GlcNAc transferase (OGT), and is found exclusively in the nucleus or cytoplasm of the cell. By using computational methods like Netphos 2.0 and Yinoyang 1.2 we found that OGT, Aurora B kinase and OGT, Death-associated protein (DAP)-like kinase, work together in a Yin Yang way, and thereby control specific checkpoints during mitosis. Bioinformatics tool, thus, are very helpful to elucidate the function of the protein by predicting the PTMs in proteins

Learning Action-oriented grasping for manipulation

Complex manipulation tasks require grasping strategies that simultaneously satisfy the stability and the semantic constraints that have to be satisfied for an action to be feasible, referred as action-oriented semantic grasp strategies. This study develops a framework using machine learning techniques to compute action-oriented semantic grasps. It takes a 3D model of the object and the action to be performed as input and provides a vector of action-oriented semantic grasps. We evaluate the performance of machine learning (particu- larly classification techniques) to determine which approaches perform better for this problem. Using the best approaches, a multi-model classification technique is developed. The proposed approach is evaluated in simulation to grasp different kitchenobjects using a parallel gripper. The results show that multi-model classification approach enhances the prediction accuracy. The implemented system can be used as to automate the data labeling process required for deep learning approaches.Peer Reviewe

Learning Action-oriented grasping for manipulation

Complex manipulation tasks require grasping strategies that simultaneously satisfy the stability and the semantic constraints that have to be satisfied for an action to be feasible, referred as action-oriented semantic grasp strategies. This study develops a framework using machine learning techniques to compute action-oriented semantic grasps. It takes a 3D model of the object and the action to be performed as input and provides a vector of action-oriented semantic grasps. We evaluate the performance of machine learning (particu- larly classification techniques) to determine which approaches perform better for this problem. Using the best approaches, a multi-model classification technique is developed. The proposed approach is evaluated in simulation to grasp different kitchenobjects using a parallel gripper. The results show that multi-model classification approach enhances the prediction accuracy. The implemented system can be used as to automate the data labeling process required for deep learning approaches.Peer Reviewe

Learning Action-oriented grasping for manipulation

Complex manipulation tasks require grasping strategies that simultaneously satisfy the stability and the semantic constraints that have to be satisfied for an action to be feasible, referred as action-oriented semantic grasp strategies. This study develops a framework using machine learning techniques to compute action-oriented semantic grasps. It takes a 3D model of the object and the action to be performed as input and provides a vector of action-oriented semantic grasps. We evaluate the performance of machine learning (particu- larly classification techniques) to determine which approaches perform better for this problem. Using the best approaches, a multi-model classification technique is developed. The proposed approach is evaluated in simulation to grasp different kitchenobjects using a parallel gripper. The results show that multi-model classification approach enhances the prediction accuracy. The implemented system can be used as to automate the data labeling process required for deep learning approaches.Peer ReviewedPostprint (author's final draft

GEARS: A Genetic Algorithm Based Machine Learning Technique to Develop Prediction Models

Abstract.-The development of new prediction models to identify potential modified residues are based on different machine learning methods. Primary sequences, biochemical properties of the amino acids and 3D structural information of proteins are used to evolve prediction models. The information about the significant residues to govern different biological processes has not been considered yet to develop a prediction model. MAPRes is an efficient tool which has been utilized to mine significant residues and association patterns for surrounding amino acids of some specific modifications on hydroxyl and amino group such as phosphorylation and acetylation. The primary sequences of the proteins and association patterns of surrounding amino acids of modified residues may use to train new dataset for the development of an efficient and reliable prediction model. Biophysical and biochemical properties of the amino acids are also important parameters for the prediction of the modified residues. This study proposes, GEARS (Genetic Evolution of ClAssifers by Learning Residue Rules and Sequences), a classifier rule learning model, which considered different machine learning techniques such as ANNs, HMM and MAPRes were considered for the development of GEARS model. The GEARS, by combining these models, will have the capacity to reduce the false negative and positive predictions

Consensus Sequences as Targets for Phosphorylation of Amino Acids in Phosphoproteins: Statistical Computing Analysis

Abstract.-Phosphorylation is a post-translational modification (PTM) of a protein on hydroxyl function of Ser/Thr/Tyr (O-linked phosphorylation) or on amino function of His (N-linked phosphorylation) by a phosphate group utilising a specific kinase. Post translational modification has effect on protein conformation and as a consequence results in alteration of protein's function. Present study analyzes the putative target sequence of O-phosphorylation of Ser, Thr and Tyr. The objective is to evaluate the preferential pattern of amino acids on both (right and left) sides of O-phosphorylated amino acid. Calculation of frequency of occurrence of each amino acid around O-linked phosphorylated amino acid residue followed by deviation parameter will serve as the source to define preferential behaviour of each amino acid to develop possible consensus prerequisite pattern for O-linked phosphorylation. These patterns will provide with means to predict phosphorylation potential of proteins and its multifunctional nature based on dynamic and reversible phosphorylation at specific sites

Phosphoproteome sequence analysis and significance: mining association patterns around phosphorylation sites utilizing MAPRes

Phosphorylation, one of the most common protein post-translational modifications (PTMs) on hydroxyl groups of S/T/Y is catalyzed by kinases and involves the presence or absence of certain amino acid residues in the vicinity of the phosphorylation sites. Using MAPRes, we have analyzed the substrate proteins of Phospho.ELM 7.0 and found that there are both general and specific requirements for the presence or absence of particular amino acids in the vicinity of phosphorylated S/T/Y for both of the phosphorylation data, whether or not kinase information was taken into account. Patterns extracted by MAPRes for kinase-specific data have been utilized to find the consensus sequence motifs for various kinases required to catalyze the process of phosphorylation on S/T/Y. These consensus sequences for different kinase groups, families, and individual members are consistent with those described earlier with some novel consensus reported for the first time. A comparison study for the patterns mined by MAPRes with the results of existing prediction methods was performed by searching for these patterns in the vicinity of phosphorylation sites predicted by different available method. This comparison resulted in 87-98% conformity with the results of the predictions by available methods. Additionally, the patterns mined by MAPRes for substrate sites included 61 kinases, the highest number analyzed so far

MAPRes: Mining association patterns among preferred amino acid residues in the vicinity of amino acids targeted for post-translational modifications

Post-translational modification (PTM) of a protein is an important event in regulating cellular functions. An algorithm, MAPRes, has been developed for mining associations among PTM sites and the preferred amino acids in their vicinity. The algorithm has been implemented to O-glycosylation and O-phosphorylation data (phosphorylated/glycosylated Ser/Thr/Tyr). The association patterns mined by MAPRes demonstrate significant correlations and the results are in conformity with the existing methods. These association rules/patterns will be helpful in predicting the sequences/motifs involved for specific PTMs in proteins