Dynamic Motion Modelling for Legged Robots

An accurate motion model is an important component in modern-day robotic systems, but building such a model for a complex system often requires an appreciable amount of manual effort. In this paper we present a motion model representation, the Dynamic Gaussian Mixture Model (DGMM), that alleviates the need to manually design the form of a motion model, and provides a direct means of incorporating auxiliary sensory data into the model. This representation and its accompanying algorithms are validated experimentally using an 8-legged kinematically complex robot, as well as a standard benchmark dataset. The presented method not only learns the robot's motion model, but also improves the model's accuracy by incorporating information about the terrain surrounding the robot

Learning and Using Multimodal Stochastic Models : A Unified Approach

This dissertation presents a principled approach to representing and using instance-based knowledge. Perceptions and actions are probabilistically modelled in a unified structure which allows for simultaneous perception modelling and reasoning about desired actions. In particular, a new method for online instance-based learning of such models is presented and analyzed. This method, called Dynamic Gaussian Mixture Estimation (DGME), adapts a model's complexity to the process being modelled. The models produced by DGME are evaluated on several classification, prediction, and control applications, and its characteristics are compared with other state-of-the-art methods. In the context of control applications, an additional novel method, Gaussian Mixture Control (GMC), is introduced for precisely controlling systems that exhibit multimodality

Expressions 2020

https://openspace.dmacc.edu/expressions/1036/thumbnail.jp

Instructional Models for Course-Based Research Experience (CRE) Teaching

The course-based research experience (CRE) with its documented educational benefits is increasingly being implemented in science, technology, engineering, and mathematics education. This article reports on a study that was done over a period of 3 years to explicate the instructional processes involved in teaching an undergraduate CRE. One hundred and two instructors from the established and large multi-institutional SEA-PHAGES program were surveyed for their understanding of the aims and practices of CRE teaching. This was followed by large-scale feedback sessions with the cohort of instructors at the annual SEA Faculty Meeting and subsequently with a small focus group of expert CRE instructors. Using a qualitative content analysis approach, the survey data were analyzed for the aims of inquiry instruction and pedagogical practices used to achieve these goals. The results characterize CRE inquiry teaching as involving three instructional models: 1) being a scientist and generating data; 2) teaching procedural knowledge; and 3) fostering project ownership. Each of these models is explicated and visualized in terms of the specific pedagogical practices and their relationships. The models present a complex picture of the ways in which CRE instruction is conducted on a daily basis and can inform instructors and institutions new to CRE teaching

Fluorogenic Substrates for In Situ Monitoring of Caspase-3 Activity in Live Cells

The in situ detection of caspase-3 activity has applications in the imaging and monitoring of multiple pathologies, notably cancer. A series of cell penetrating FRET-based fluorogenic substrates were designed and synthesised for the detection of caspase-3 in live cells. A variety of modifications of the classical caspase-3 and caspase-7 substrate sequence Asp-Glu-Val-Asp were carried out in order to increase caspase-3 affinity and eliminate caspase-7 cross-reactivity. To allow cellular uptake and good solubility, the substrates were conjugated to a cationic peptoid. The most selective fluorogenic substrate 27, FAM-Ahx-Asp-Leu-Pro-Asp-Lys(MR)-Ahx, conjugated to the cell penetrating peptoid at the C-terminus, was able to detect and quantify caspase-3 activity in apoptotic cells without cross-reactivity by caspase-7.This work was supported by the Ramon Areces and Caja Madrid Foundations to AMPL and Spanish Ministry of Economy and Competitiveness to MLSG (graduate student fellowships FPI BES-2010-030257 and EEBB-I-13-07131)

Eliminating caspase-7 and cathepsin B cross-reactivity on fluorogenic caspase-3 substrates

11 FRET-based fluorogenic substrates were constructed using the pentapeptide template Asp-Glu-X(2)-Asp-X(1)′, and evaluated with caspase-3, caspase-7 and cathepsin B. The sequence Asp-Glu-Pro-Asp-Ser was able to selectively quantify caspase-3 activity in vitro without notable caspase-7 and cathepsin B cross-reactivity, while exhibiting low μM K (M) values and good catalytic efficiencies (7.0–16.9 μM(–1) min(–1))

Models of classroom assessment for course-based research experiences

Course-based research pedagogy involves positioning students as contributors to authentic research projects as part of an engaging educational experience that promotes their learning and persistence in science. To develop a model for assessing and grading students engaged in this type of learning experience, the assessment aims and practices of a community of experienced course-based research instructors were collected and analyzed. This approach defines four aims of course-based research assessment—(1) Assessing Laboratory Work and Scientific Thinking; (2) Evaluating Mastery of Concepts, Quantitative Thinking and Skills; (3) Appraising Forms of Scientific Communication; and (4) Metacognition of Learning—along with a set of practices for each aim. These aims and practices of assessment were then integrated with previously developed models of course-based research instruction to reveal an assessment program in which instructors provide extensive feedback to support productive student engagement in research while grading those aspects of research that are necessary for the student to succeed. Assessment conducted in this way delicately balances the need to facilitate students’ ongoing research with the requirement of a final grade without undercutting the important aims of a CRE education

Erlernen und Anwendung von multimodalen stochastischen Modellen : ein einheitlicher Ansatz

This dissertation presents a principled approach to representing and using instance-based knowledge. Perceptions and actions are probabilistically modelled in a unified structure which allows for simultaneous perception modelling and reasoning about desired actions. In particular, a new method for online instance-based learning of such models is presented and analyzed. This method, called Dynamic Gaussian Mixture Estimation (DGME), adapts a model's complexity to the process being modelled. The models produced by DGME are evaluated on several classification, prediction, and control applications, and its characteristics are compared with other state-of-the-art methods. In the context of control applications, an additional novel method, Gaussian Mixture Control (GMC), is introduced for precisely controlling systems that exhibit multimodality

Dynamic Motion Modelling for Legged Robots

Abstract-An accurate motion model is an important component in modern-day robotic systems, but building such a model for a complex system often requires an appreciable amount of manual effort. In this paper we present a motion model representation, the Dynamic Gaussian Mixture Model (DGMM), that alleviates the need to manually design the form of a motion model, and provides a direct means of incorporating auxiliary sensory data into the model. This representation and its accompanying algorithms are validated experimentally using an 8-legged kinematically complex robot, as well as a standard benchmark dataset. The presented method not only learns the robot's motion model, but also improves the model's accuracy by incorporating information about the terrain surrounding the robot