Contingent task and motion planning under uncertainty for human–robot interactions

Manipulation planning under incomplete information is a highly challenging task for mobile manipulators. Uncertainty can be resolved by robot perception modules or using human knowledge in the execution process. Human operators can also collaborate with robots for the execution of some difficult actions or as helpers in sharing the task knowledge. In this scope, a contingent-based task and motion planning is proposed taking into account robot uncertainty and human–robot interactions, resulting a tree-shaped set of geometrically feasible plans. Different sorts of geometric reasoning processes are embedded inside the planner to cope with task constraints like detecting occluding objects when a robot needs to grasp an object. The proposal has been evaluated with different challenging scenarios in simulation and a real environment.Postprint (published version

PMK : a knowledge processing framework for autonomous robotics perception and manipulation

Autonomous indoor service robots are supposed to accomplish tasks, like serve a cup, which involve manipulation actions. Particularly, for complex manipulation tasks which are subject to geometric constraints, spatial information and a rich semantic knowledge about objects, types, and functionality are required, together with the way in which these objects can be manipulated. In this line, this paper presents an ontological-based reasoning framework called Perception and Manipulation Knowledge (PMK) that includes: (1) the modeling of the environment in a standardized way to provide common vocabularies for information exchange in human-robot or robot-robot collaboration, (2) a sensory module to perceive the objects in the environment and assert the ontological knowledge, (3) an evaluation-based analysis of the situation of the objects in the environment, in order to enhance the planning of manipulation tasks. The paper describes the concepts and the implementation of PMK, and presents an example demonstrating the range of information the framework can provide for autonomous robots.Peer ReviewedPostprint (published version

The effect of using flipped classroom instruction on students’ achievement in the new 2016 scholastic assessment test mathematics skills in the United Arab Emirates

The flipped classroom instruction considered as the focus of many researchers and teachers in the recent years, many teachers around the word tried the flipped classroom instructions in different ways, different tools, to teach different subjects with different grades. Taking in the consideration, applying the flipped classroom teaching methods needs a lot of preparation and technological tools. This study utilized a quasiexperimental method research design to investigate the effect of flipped classroom instruction on students’ achievements in the new SAT 2016 mathematics skills (Heart of Algebra, Problem solving and data analysis, and Passport to Advanced Math) for the eleventh grade Emirati, female students in Al Ain, United Arab Emirates. The purpose of this study was to determine if there was a statistically significant difference in student achievements in the new SAT mathematics skills between two groups of grade 11 students, the experimental group was taught by flipped classroom instruction, and the control group was taught by ordinary teaching methods. The result of the posttest showed a statistically significant point of preference for the experimental group over the control group in all of the new SAT mathematics skills except the problem solving and data analysis skills. Finally, study findings suggest that teachers who are teaching mathematics standardized test skills like SAT may use flipped classroom instruction to increase the students’ readiness and to improve the students’ thinking skills to simulate the 21st-century skills. After offering a proper training and professional development courses in the best practice of flipped classroom instruction

Recover: A Neuro-Symbolic Framework for Failure Detection and Recovery

Recognizing failures during task execution and implementing recovery procedures is challenging in robotics. Traditional approaches rely on the availability of extensive data or a tight set of constraints, while more recent approaches leverage large language models (LLMs) to verify task steps and replan accordingly. However, these methods often operate offline, necessitating scene resets and incurring in high costs. This paper introduces Recover, a neuro-symbolic framework for online failure identification and recovery. By integrating ontologies, logical rules, and LLM-based planners, Recover exploits symbolic information to enhance the ability of LLMs to generate recovery plans and also to decrease the associated costs. In order to demonstrate the capabilities of our method in a simulated kitchen environment, we introduce OntoThor, an ontology describing the AI2Thor simulator setting. Empirical evaluation shows that OntoThor's logical rules accurately detect all failures in the analyzed tasks, and that Recover considerably outperforms, for both failure detection and recovery, a baseline method reliant solely on LLMs

U-DeepONet: U-Net Enhanced Deep Operator Network for Geologic Carbon Sequestration

FNO and DeepONet are by far the most popular neural operator learning algorithms. FNO seems to enjoy an edge in popularity due to its ease of use, especially with high dimensional data. However, a lesser-acknowledged feature of DeepONet is its modularity. This feature allows the user the flexibility of choosing the kind of neural network to be used in the trunk and/or branch of the DeepONet. This is beneficial because it has been shown many times that different types of problems require different kinds of network architectures for effective learning. In this work, we will take advantage of this feature by carefully designing a more efficient neural operator based on the DeepONet architecture. We introduce U-Net enhanced DeepONet (U-DeepONet) for learning the solution operator of highly complex CO2-water two-phase flow in heterogeneous porous media. The U-DeepONet is more accurate in predicting gas saturation and pressure buildup than the state-of-the-art U-Net based Fourier Neural Operator (U-FNO) and the Fourier-enhanced Multiple-Input Operator (Fourier-MIONet) trained on the same dataset. In addition, the proposed U-DeepONet is significantly more efficient in training times than both the U-FNO (more than 18 times faster) and the Fourier-MIONet (more than 5 times faster), while consuming less computational resources. We also show that the U-DeepONet is more data efficient and better at generalization than both the U-FNO and the Fourier-MIONet

Automatic generation of behavior trees for the execution of robotic manipulation tasks

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksRobots should be able to exercise reasoning in both symbolic and geometric levels in order to plan a manipulation task. The execution of such tasks needs to be robust enough to cope with real environments. In an attempt to address this pertinent industry need, the paper proposes the use of behavior trees for effective robotic manipulation in dynamic environments. This paper presents a method to automatically generate a behavior tree and showcases its ability to enable the robot to reason at different levels and adapt to an uncertain and changing environment. This allows for a complex task to be robustly executed, pioneering the advancement towards fully functional service robots.Peer ReviewedPostprint (author's final draft

Reasoning and state monitoring for the robust execution of robotic manipulation tasks

The execution of robotic manipulation tasks needs to be robust in front of failures or changes in the environment, and for this purpose, Behavior Trees (BT) are a good alternative to Finite State Machines, because the ability of BTs to be edited during run time and the fact that one can design reactive systems with BTs, makes the BT executor a robust execution manager. However, the good monitoring of the system state is required in order to react to errors at either geometric or symbolic level requiring, respectively, replanning at motion or at task level. This paper make a proposal in this line and, moreover, makes task planning adaptive to the actual situations encountered by knowledge-based reasoning procedures to automatically generate the Planning Domain Definition Language (PDDL) files that define the task.Peer ReviewedPostprint (published version

The principles of measurement and evaluation from the perspective of the Holy Quran (analytic study)

This study aims at finding out the principles of measurement and evaluation from the perspective of the Holy Koran through analyzing the content of Koranic verses related to the terminology of measurement and evaluation.  The study is distinguished from other studies in that it elicits fixed principles and characteristics of measurement and evaluation directly from the Koran and away of humanistic interpretations that are liable to change now and then. The results of the study show that there are a number of permanent and comprehensive basics of measurement and evaluation including: the principle of purposeful creation and succession in the ground, and the principle of unity, and  balance. The study also reveals that there are many characteristics related to measurement and evaluation. That is, it is a process which is comprehensive, balanced, objective, flexible, positively and cooperative in which all concerned  parties take part. It aims at well-function, is related to morals, and is a permanent process. In the light of study findings a number of recommendations are suggested. Keywords:  Measurement and Evaluation, the Holy Koran.

Antioxidant Categories and Mode of Action

Oxidative stress has received a considerable scientific attention as a mediator in the etiology of many human diseases. Oxidative stress is the result of an imbalance between free radicals and antioxidants. Cells can be damaged by free radicals that are considered to play a main role in the aging process and diseases development. Antioxidants are the first line of defense against the detrimental effects of free radical damage, and it is essential to maintain optimal health via different mechanisms of action. Types of antioxidants range from those generated endogenously by the body cells, to exogenous agents such as dietary supplements. Antioxidant insufficiency can be developed as a result of decreased antioxidant intake, synthesis of endogenous enzymes, or increased antioxidant utilization. To maintain optimal body function, antioxidant supplementation has become an increasingly popular practice through improving free radical protection. In this chapter, we first elucidate the oxidative stress, and then define the antioxidant and its categories. Finally, introduce the antioxidants mode of actions for cell protection from free radicals