Learning Sensor Feedback Models from Demonstrations via Phase-Modulated Neural Networks

In order to robustly execute a task under environmental uncertainty, a robot needs to be able to reactively adapt to changes arising in its environment. The environment changes are usually reflected in deviation from expected sensory traces. These deviations in sensory traces can be used to drive the motion adaptation, and for this purpose, a feedback model is required. The feedback model maps the deviations in sensory traces to the motion plan adaptation. In this paper, we develop a general data-driven framework for learning a feedback model from demonstrations. We utilize a variant of a radial basis function network structure --with movement phases as kernel centers-- which can generally be applied to represent any feedback models for movement primitives. To demonstrate the effectiveness of our framework, we test it on the task of scraping on a tilt board. In this task, we are learning a reactive policy in the form of orientation adaptation, based on deviations of tactile sensor traces. As a proof of concept of our method, we provide evaluations on an anthropomorphic robot. A video demonstrating our approach and its results can be seen in https://youtu.be/7Dx5imy1KcwComment: 8 pages, accepted to be published at the International Conference on Robotics and Automation (ICRA) 201

Human-Machine Interface for Remote Training of Robot Tasks

Regardless of their industrial or research application, the streamlining of robot operations is limited by the proximity of experienced users to the actual hardware. Be it massive open online robotics courses, crowd-sourcing of robot task training, or remote research on massive robot farms for machine learning, the need to create an apt remote Human-Machine Interface is quite prevalent. The paper at hand proposes a novel solution to the programming/training of remote robots employing an intuitive and accurate user-interface which offers all the benefits of working with real robots without imposing delays and inefficiency. The system includes: a vision-based 3D hand detection and gesture recognition subsystem, a simulated digital twin of a robot as visual feedback, and the "remote" robot learning/executing trajectories using dynamic motion primitives. Our results indicate that the system is a promising solution to the problem of remote training of robot tasks.Comment: Accepted in IEEE International Conference on Imaging Systems and Techniques - IST201

Regression between headmaster leadership, task load and job satisfaction of special education integration program teacher

Managing school is a daunting task for a headmaster. This responsibility is exacerbated when it involves the Special Education Integration Program (SEIP). This situation requires appropriate and effective leadership in addressing some of the issues that are currently taking place at SEIP such as task load and job satisfaction. This study aimed to identify the influence of headmaster leadership on task load and teacher job satisfaction at SEIP. This quantitative study was conducted by distributing 400 sets of randomized questionnaires to SEIP teachers across Malaysia through google form. The data obtained were then analyzed using Structural Equation Modeling (SEM) and AMOS software. The results show that there is a significant positive effect on the leadership of the headmaster and the task load of the teacher. Likewise, the construct of task load and teacher job satisfaction has a significant positive effect. However, for the construct of headmaster leadership and teacher job satisfaction, there was no significant positive relationship. This finding is very important as a reference to the school administration re-evaluating their leadership so as not to burden SEIP teachers and to give them job satisfaction. In addition, the findings of this study can also serve as a guide for SEIP teachers to increase awareness of the importance of managing their tasks. This study also focused on education leadership in general and more specifically on special education leadership

Machine Design Experiments Using Gears to Foster Discovery Learning

Machine Design Experiments Using Gears to Foster Discovery Learning For the typical undergraduate engineering student the topic of gears is introduced and discussed in several courses. Early exposure may be in a physics course or in a first dynamics course,where gear pairs are presented as an idealized means to change speed ratios and torque ratios.They are used for mechanical advantage or to achieve desired speed, and the focus is usually on kinematics. Since gears have inertia they store kinetic energy and are part of the dynamic equations of motion of mechanisms and machines. For mechanical engineering students, gears are a core component studied in courses such as \u27kinematics and dynamics of mechanisms\u27 and \u27machine design\u27, where the nomenclature and design equations are developed for various types of gears. There may be exposure to real gears in a mechanical engineering laboratory; more often, students may see gears passed around in class and as part of demonstrations.In this paper we describe new experiments that were designed to provide mechanical engineering students with discovery learning experiences with gears and mechanical systems using gears.The suite of practical experiments presents students with a range of challenges that require them to analyze, measure, design, and fabricate gears. Activities in the experiments include: (1) Identifying gear types (spur, helical, bevel, etc.) and appropriate applications (automotive transmissions and differentials, drills, gear head motors). (2) Disassembling and re-assembling a kitchen mixer (with design and manufacturing questions related to its gears). (3) Disassembling and re-assembling an automotive HVAC baffle sub-assembly (with measurement of train ratios, and design and manufacturing questions related to its gears). (4) Designing the gear mechanism for driving the minute and hour hands of a gear clock given a known yet arbitrary drive speed. Fabricating the gears of the clock via rapid prototyping (3D printing), assembling the clock, and then testing the timing accuracy.In addition to reporting the details of the experiments, we share experiences of students and teaching assistants in their use and effectiveness. We provide insights into how well students became familiar with types and nomenclature of gears and understood the applicability of different gears to actual real-world problems. The intent of the experiments is to effectively enhance mechanical engineering students\u27 awareness of gears and expand their knowledge and confidence in the use of gears in machine and mechanism design

Comparative evaluation of approaches in T.4.1-4.3 and working definition of adaptive module

The goal of this deliverable is two-fold: (1) to present and compare different approaches towards learning and encoding movements us- ing dynamical systems that have been developed by the AMARSi partners (in the past during the first 6 months of the project), and (2) to analyze their suitability to be used as adaptive modules, i.e. as building blocks for the complete architecture that will be devel- oped in the project. The document presents a total of eight approaches, in two groups: modules for discrete movements (i.e. with a clear goal where the movement stops) and for rhythmic movements (i.e. which exhibit periodicity). The basic formulation of each approach is presented together with some illustrative simulation results. Key character- istics such as the type of dynamical behavior, learning algorithm, generalization properties, stability analysis are then discussed for each approach. We then make a comparative analysis of the different approaches by comparing these characteristics and discussing their suitability for the AMARSi project

Learning Task Priorities from Demonstrations

Bimanual operations in humanoids offer the possibility to carry out more than one manipulation task at the same time, which in turn introduces the problem of task prioritization. We address this problem from a learning from demonstration perspective, by extending the Task-Parameterized Gaussian Mixture Model (TP-GMM) to Jacobian and null space structures. The proposed approach is tested on bimanual skills but can be applied in any scenario where the prioritization between potentially conflicting tasks needs to be learned. We evaluate the proposed framework in: two different tasks with humanoids requiring the learning of priorities and a loco-manipulation scenario, showing that the approach can be exploited to learn the prioritization of multiple tasks in parallel.Comment: Accepted for publication at the IEEE Transactions on Robotic