Differences in Learning from Complex Versus Simple Visual Interfaces When Operating a Model Excavator

The goal of this study was to test two visual co-robot interfaces (one simple and one more complex) and their effectiveness in teaching a novice participant to operate a complex machine at a later date without assistance. Participants (N = 113) were randomly assigned to one of three groups (one with a basic user interface, one with a more complex guidance interface, and one without an interface) to test the teaching ability of the co-robot in training the user to perform a task with a remote-controlled excavator. Each group was asked to load dirt from a bin into a small model dump truck (in scale with the excavator) with the help of the robot instructor and were asked to return a few days later to complete the task again without the robot instructor. Trials were monitored for completion time and errors and compared to those of an expert operator. The result was that the simple interface was slightly more effective than the more complex version at teaching humans a complicated task. This suggests that novices may learn better and retain more information when given basic feedback (using operant conditioning principles) and less guidance from robot teachers. As robots are increasingly used to help humans learn skills, industries may benefit from simpler guided instructions rather than more complex versions. Such changes in training may result in improved situational awareness and increased safety in the workplace.Psycholog

Digging Deeper into the Methods of Computational Chemistry

This dissertation applies a skeptical but hopeful analytical paradigm and the tools of linear algebra, numerical methods, and machine learning to a diversity of problems in computational chemistry. When the foundation underlying a project is undermined, the primary purpose of the project becomes digging into the nature and structure of the problem. A common theme emerges in which assumptions in an area are challenged and a deeper understanding of the problem structure leads to new insights. In chapter 2, this approach is exploited to approximate derivative coupling vectors, which together with the difference gradient span the branching planes of conical intersections between electronic states. While gradients are commonly available in many electronic structure methods, the derivative coupling vectors are not always implemented and ready for use in characterizing conical intersections. An approach is introduced which computes the derivative coupling vector with high accuracy (direction and magnitude) using energy and gradient information. The new method is based on the combination of a linear-coupling two-state Hamiltonian and a finite-difference Davidson approach for computing the branching plane. Benchmark cases are provided showing these vectors can be efficiently computed near conical intersections. In chapter 3, this approach yields a countercultural explanation for what machine learning algorithms have learned in modeling a chemical reactivity dataset. Data-driven models of chemical reactions, a departure from conventional chemical approaches, have recently been shown to be statistically successful using machine learning. These models, however, are largely black box in character and have not provided the kind of chemical insights that historically advanced the field of chemistry. The chapter examines the knowledgebase of machine learning models—what does the machine learn?—by deconstructing black box machine learning models of a diverse chemical reaction dataset. Through experimentation with chemical representations and modeling techniques, the analysis provides insights into the nature of how statistical accuracy can arise, even when the model lacks informative physical principles. By peeling back the layers of these complicated models we arrive at a minimal, chemically intuitive model (and no machine learning involved). This model is based on systematic reaction type classification and Evans-Polanyi relationships within reaction types which are easily visualized and interpreted. Through exploring this simple model, we gain deeper understanding of the dataset and uncover a means for expert interactions to improve the model’s reliability. In chapter 4, human - algorithm interaction is explored as a paradigm for generating representative ensembles of conformers for organic compounds, a challenging problem in computational chemistry with implications on the ability to understand and predict reactivity. The approach utilizes the molecular editor IQmol as an interface between chemists and reinforcement learning algorithms with the cheminformatics package RDKit as a backbone. Conformer ensembles are evaluated by uniqueness and the approximation they yield of the partition function. Prototype results are presented for a standard reinforcement learning algorithm tested on linear alkanes and chemist manipulation of a fragment of the biomolecule lignin. Future aims and directions for this young project are discussed. The concluding chapter reflects on the broader lessons learned from conducting the dissertation. It discusses open questions and potential paradigms for pursuing them.PHDChemistryUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155137/1/joshkamm_1.pd

Vjerojatnosni model robotskoga djelovanja u fizičkoj interakciji s čovjekom

U doktorskom radu razvijen je vjerojatnosni model pomoću kojeg robot donosi odluke o svojem djelovanju putem fizičke interakcije s čovjekom. Klasifikacijom taktilnih podražaja na temelju kapacitivnog senzora, sile i prostornog položaja razaznaju se elementi i smisao interakcije. Kako bi model imao određenu autonomiju i mogućnost kretanja kroz prostor u sklopu istraživanja obrađen je problem prostornog kretanja. U sklopu istraživanja definirana je višekriterijska interpretacija radnog prostora u kojoj postoji distinkcija između objekata u okolini, čovjeka, ciljeva, samog robota te putanja robota. Model interakcije je oblikovan kao slijed radnji koje robot izvršava što u konačnici rezultira robotskim djelovanjem. Definiranje varijabli vjerojatnosti modela proizlazi iz interakcije s čovjekom. Naučeni obrasci predstavljaju dugoročno znanje na temelju kojih se oblikuje robotsko djelovanje u skladu s trenutnim stanjem okoline. Vremenskim razlikovanjem bližim događajima pridaje se značajno veći faktor utjecaja, a onim udaljenijim u prošlost mnogo manji. U laboratorijskim uvjetima provedeni su pokusi na realnom sustavu koji čine robotska ruka s integriranim senzorima momenata i upravljačkom jedinicom, računalo, kao i „umjetna koža“ koja posjeduje mogućnost razlučivanja ljudskog dodira i neposredne blizine prvenstveno biološkog materijala. Eksperimentima su utvrđena ograničenja primjene autonomnog djelovanja robota