Colloidal particles at a nematic-isotropic interface: effects of confinement

When captured by a flat nematic-isotropic interface, colloidal particles can be dragged by it. As a result spatially periodic structures may appear, with the period depending on a particle mass, size, and interface velocity~\cite{west.jl:2002}. If liquid crystal is sandwiched between two substrates, the interface takes a wedge-like shape, accommodating the interface-substrate contact angle and minimizing the director distortions on its nematic side. Correspondingly, particles move along complex trajectories: they are first captured by the interface and then `glide' towards its vertex point. Our experiments quantify this scenario, and numerical minimization of the Landau-de Gennes free energy allow for a qualitative description of the interfacial structure and the drag force.Comment: 7 pages, 9 figure

Enhancing Perceived Safety in Human–Robot Collaborative Construction Using Immersive Virtual Environments

Advances in robotics now permit humans to work collaboratively with robots. However, humans often feel unsafe working alongside robots. Our knowledge of how to help humans overcome this issue is limited by two challenges. One, it is difficult, expensive and time-consuming to prototype robots and set up various work situations needed to conduct studies in this area. Two, we lack strong theoretical models to predict and explain perceived safety and its influence on human–robot work collaboration (HRWC). To address these issues, we introduce the Robot Acceptance Safety Model (RASM) and employ immersive virtual environments (IVEs) to examine perceived safety of working on tasks alongside a robot. Results from a between-subjects experiment done in an IVE show that separation of work areas between robots and humans increases perceived safety by promoting team identification and trust in the robot. In addition, the more participants felt it was safe to work with the robot, the more willing they were to work alongside the robot in the future.University of Michigan Mcubed Grant: Virtual Prototyping of Human-Robot Collaboration in Unstructured Construction EnvironmentsPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/145620/1/You et al. forthcoming in AutCon.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/145620/4/You et al. 2018.pdfDescription of You et al. 2018.pdf : Published Versio

Effect of force on mononucleosomal dynamics

Using single-molecule optical-trapping techniques, we examined the force-induced dynamic behavior of a single nucleosome core particle. Our experiments using the DNA construct containing the 601 nucleosome-positioning sequence revealed that the nucleosome unravels in at least two major stages. The first stage, which we attributed to the unraveling of the first DNA wrap around the histone octamer, could be mechanically induced in a reversible manner, and when kept at constant force within a critical force range, exhibited two-state hopping behavior. From the hopping data, we determined the force-dependent equilibrium constant and rates for opening/closing of the outer wrap. Our investigation of the second unraveling event at various loading rates, which we attributed to the inner DNA wrap, revealed that this unraveling event cannot be described as a simple two-state process. We also looked at the behavior of the mononucleosome in a high-salt buffer, which revealed that the outer DNA wrap is more sensitive to changes in the ionic environment than the inner DNA wrap. These findings are needed to understand the energetics of nucleosome remodeling

Semi-supervised Learning From Demonstration through Program Synthesis: An Inspection Robot Case Study

Semi-supervised learning improves the performance of supervised machine learning by leveraging methods from unsupervised learning to extract information not explicitly available in the labels. Through the design of a system that enables a robot to learn inspection strategies from a human operator, we present a hybrid semi-supervised system capable of learning interpretable and verifiable models from demonstrations. The system induces a controller program by learning from immersive demonstrations using sequential importance sampling. These visual servo controllers are parametrised by proportional gains and are visually verifiable through observation of the position of the robot in the environment. Clustering and effective particle size filtering allows the system to discover goals in the state space. These goals are used to label the original demonstration for end-to-end learning of behavioural models. The behavioural models are used for autonomous model predictive control and scrutinised for explanations. We implement causal sensitivity analysis to identify salient objects and generate counterfactual conditional explanations. These features enable decision making interpretation and post hoc discovery of the causes of a failure. The proposed system expands on previous approaches to program synthesis by incorporating repellers in the attribution prior of the sampling process. We successfully learn the hybrid system from an inspection scenario where an unmanned ground vehicle has to inspect, in a specific order, different areas of the environment. The system induces an interpretable computer program of the demonstration that can be synthesised to produce novel inspection behaviours. Importantly, the robot successfully runs the synthesised program on an unseen configuration of the environment while presenting explanations of its autonomous behaviour.Comment: In Proceedings AREA 2020, arXiv:2007.1126