Complex Manipulation Research Articles

Enhancement of cyanobacterial PHB production using random chemical mutagenesis with detection through FACS.

Poly-hydroxy-butyrate (PHB) bioplastic resin can be made directly from atmospheric CO2 using cyanobacteria. However, higher PHB productivities are required before large-scale production is economically viable. Random mutagenesis offers a way to create new production strains with increased PHB yields and increased biomass densities without complex technical manipulation associated with genetically modified organisms. This study used staining with lipid fluorescent dye (BODIPY 493/593) and fluorescence-activated cell sorting (FACS) to select high lipid content mutants and followed this with a well plate growth screen. Thirteen mutants were selected for flask cultivation and two strains produced significantly higher PHB yields (29% and 26% higher than wild type), biomass accumulation (36% and 33% higher than wild type) and volumetric PHB density (75% and 67% higher than wild type). The maximum PHB yielding strain (% dcw) was 12.0%, which was 43% higher than the wild type (8.3% in this study). The highest volumetric PHB density was 18.8mg PHB/L compared to 10.7mg PHB/L by the wild type. To develop cyanobacterial strain with higher PHB productivities, the combination of random chemical mutagenesis and FACS holds great potential to promote cyanobacteria bioplastic production becoming economically viable.

A Comprehensive Review of Vision-Based Robotic Applications: Current State, Components, Approaches, Barriers, and Potential Solutions

Being an emerging technology, robotic manipulation has encountered tremendous advancements due to technological developments starting from using sensors to artificial intelligence. Over the decades, robotic manipulation has advanced in terms of the versatility and flexibility of mobile robot platforms. Thus, robots are now capable of interacting with the world around them. To interact with the real world, robots require various sensory inputs from their surroundings, and the use of vision is rapidly increasing nowadays, as vision is unquestionably a rich source of information for a robotic system. In recent years, robotic manipulators have made significant progress towards achieving human-like abilities. There is still a large gap between human and robot dexterity, especially when it comes to executing complex and long-lasting manipulations. This paper comprehensively investigates the state-of-the-art development of vision-based robotic application, which includes the current state, components, and approaches used along with the algorithms with respect to the control and application of robots. Furthermore, a comprehensive analysis of those vision-based applied algorithms, their effectiveness, and their complexity has been enlightened here. To conclude, there is a discussion over the constraints while performing the research and potential solutions to develop a robust and accurate vision-based robot manipulation.

CRISPR-Suppressor Scanning for Systematic Discovery of Drug-Resistance Mutations.

CRISPR-Cas9 genome editing technologies have enabled complex genetic manipulations in situ, including large-scale, pooled screening approaches to probe and uncover mechanistic insights across various biological processes. The RNA-programmable nature of CRISPR-Cas9 greatly empowers tiling mutagenesis approaches to elucidate molecular details of protein function, in particular the interrogation of mechanisms of resistance to small molecules, an approach termed CRISPR-suppressor scanning. In a typical CRISPR-suppressor scanning experiment, a pooled library of single-guide RNAs is designed to target across the coding sequence(s) of one or more genes, enabling the Cas9 nuclease to systematically mutate the targeted proteins and generate large numbers of diverse protein variants in situ. This cellular pool of protein variants is then challenged with drug treatment to identify mutations conferring a fitness advantage. Drug-resistance mutations identified with this approach can not only elucidate drug mechanism of action but also reveal deeper mechanistic insights into protein structure-function relationships. In this article, we outline the framework for a standard CRISPR-suppressor scanning experiment. Specifically, we provide instructions for the design and construction of a pooled sgRNA library, execution of a CRISPR-suppressor scanning screen, and basic computational analysis of the resulting data. © 2022 Wiley Periodicals LLC. Basic Protocol 1: Design and generation of a pooled sgRNA library Support Protocol 1: sgRNA library design using command-line CRISPOR Support Protocol 2: Production and titering of pooled sgRNA library lentivirus Basic Protocol 2: Execution and analysis of a CRISPR-suppressor scanning experiment.

Plasmon-enhanced rotational dynamics of anisotropic core-shell polymeric-metallic microparticles

The development of efficient and cost-effective micromachines is a challenge for applied and fundamental science, given their wide fields of usage. Light is a suitable tool to move small objects in a noncontact way, given its capabilities in exerting forces and torques. However, when complex manipulation is required, micro-objects with proper architecture could play a specific role. Here we report on the rotational dynamics of core-shell particles, with a polymeric nematic core of ellipsoidal shape capped by Au nanoparticles. They undergo a peculiar synchronous spinning and orbital motion when irradiated by a simple Gaussian beam, which originates from the coupling of the metallic nanoparticles’ optical response and the core anisotropies. The rotation capabilities are strongly enhanced when the trapping wavelength lies in the plasmonic resonance region: indeed, the spin kinetic energy reaches values two orders of magnitude larger than the one of bare microparticles. The proposed strategy brings important insights into optimizing the design of light controlled micro-objects and might benefit applications in microfluidics, microrheology, and micromachining involving rotational dynamics.

Method for extracting the equivalent admittance from time-varying metasurfaces and its application to self-tuned spatiotemporal wave manipulation

With their self-tuned time-varying responses, waveform-selective metasurfaces embedded with nonlinear electronics have shown fascinating applications, including distinguishing different electromagnetic waves depending on the pulse width (PW). However, thus far they have only been realized with a spatially homogeneous scattering profile. Here, by modeling a metasurface as time-varying admittance sheets, we provide an analytical calculation method to predict the metasurface time-domain responses. This allows derivation of design specifications in the form of equivalent sheet admittance, which is useful in synthesizing a metasurface with spatiotemporal control, such as to realize a metasurface with prescribed time-dependent diffraction characteristics. As an example, based on the proposed equivalent admittance sheet modeling, we synthesize a waveform-selective Fresnel zone plate with variable focal length depending on the incoming PW. The proposed synthesis method for PW-dependent metasurfaces may be extended to designing metasurfaces with more complex spatiotemporal wave manipulation, benefiting applications such as sensing, wireless communications and signal processing.

Programmable Parallel Optical Logic Gates on a Multimode Waveguide Engine

Optical logic gates have been proposed and demonstrated on a function programmable waveguide engine constructed using buried silicon nitride waveguides in polymer and a set of thermal electrodes. The device can perform logic AND or OR operations for the input signals A and B, each containing two bits of information, in parallel. The input signals, in the form of binary current values in the electronic domain, are applied to a subset of thermal electrodes, while the computed logic states are converted to optical intensity variations at the single-mode waveguide outputs. The rest of the electrodes work as weights to define the device function, either AND or OR, by adjusting the light interference in the multimode waveguide through thermo-optic effect. Simulations were first performed to reveal the nonlinear response of the received light intensity with respect to the applied current, thus allowing complex and effective manipulation of the light field on the waveguide engine. After chip fabrication and system integration, 65,536 experiments were performed automatically. The data are fed into a sorting program to find the valid settings that satisfy the respective truth table out of the 283,852,800 possible input/weight/output combinations. Four cases of operations for the AND and OR gates are presented in the end, with different bar and contrast values. This simple, low-cost yet powerful engine may be further developed for applications in on-chip photonic computing and signal switching.

Using Adaptive Directed Acyclic Graph for Human In-Hand Motion Identification with Hybrid Surface Electromyography and Kinect

The multi-fingered dexterous robotic hand is increasingly used to achieve more complex and sophisticated human-like manipulation tasks on various occasions. This paper proposes a hybrid Surface Electromyography (SEMG) and Kinect-based human in-hand motion (HIM) capture system architecture for recognizing complex motions of the humans by observing the state information between an object and the human hand, then transferring the manipulation skills into bionic multi-fingered robotic hand realizing dexterous in-hand manipulation. First, an Adaptive Directed Acyclic Graph (ADAG) algorithm for recognizing HIMs is proposed and optimized based on the comparison of multi-class support vector machines; second, ten representative complex in-hand motions are demonstrated by ten subjects, and SEMG and Kinect signals are obtained based on a multi-modal data acquisition platform; then, combined with the proposed algorithm framework, a series of data preprocessing algorithms are realized. There is statistical symmetry in similar types of SEMG signals and images, and asymmetry in different types of SEMG signals and images. A detailed analysis and an in-depth discussion are given from the results of the ADAG recognizing HIMs, motion recognition rates of different perceptrons, motion recognition rates of different subjects, motion recognition rates of different multi-class SVM methods, and motion recognition rates of different machine learning methods. The results of this experiment confirm the feasibility of the proposed method, with a recognition rate of 95.10%.

A Framework of Robot Skill Learning From Complex and Long-Horizon Tasks

Robot Learning from humans is a promising paradigm for directly transferring human skills to robots. This learning allows robots to encapsulate task constraints and motion patterns from human demonstrations as well as acquire skills that can be adapted to unseen scenarios. Even though many state-of-the-art skill-learning successes have been achieved, simultaneously addressing variability from a complex and long-horizon manipulation task and generalizing it to external uncertainty remains a challenge. This efficient skill learning has to allow for handling large-scale, high-dimensional demonstrations, adapting to environmental changes (starting, via and end points and obstacles), generalizing to task constraints (trajectory precision, stiffness), and measuring uncertainty in the reproduction. To this effect, we present a novel robot skill-learning framework called SVGP-CoGP that will implement all the aforementioned properties by encoding task variability from multiple demonstrations using Sparse Variational Gaussian Processes (SVGP) and adapting to additional constraints via a coregionalized multi-output GP (CoGP) based on SVGP. The proposed method can significantly reduce the computational complexity of model fitting by making use of the variational inference of GP models, which makes it possible for robots to learn skills from complex and long-horizon tasks. We evaluated and compared the effectiveness and strengths of our framework with existing probabilistic methods on a Kinova robot that performed emergency button-pressing tasks. The results indicated that our framework allowed the robot to learn skills from complex and long-horizon manipulation tasks that outperformed baselines both in quantitative evaluation and in an online test. Note to Practitioners–The objective of this work is to address the problem of robot-learning from complex and long-horizon manipulation tasks to allow end-users to teach robots new tasks by having them learn from human demonstrations instead of being programmed. We start with a brief historical overview of widely used methods and summarize five prominent capabilities that a skill-learning approach should have: variability, uncertainty, correlation, extrapolation, and adaptability. We then propose an entirely GP-based skill-learning framework by simultaneously addressing all those capabilities by using a sparse variational Gaussian process (SVGP) in conjunction with a coregionalized multioutput GP model. The proposed framework incorporated variational inference and kernel treatments such that the robot learned skills from large-scale demonstrations and high-dimensional trajectories. Finally, experimental evaluation and performance comparisons were performed in a real robot button-pressing task, the results of which indicated that our proposed method enables robots to achieve complex and long-horizon manipulation tasks in dynamic and unstructured environments. With the rapid development of collaborative robots in service and industry, our findings have application scenarios as diverse as robot learning from demonstration, robot skill learning, human–robot collaboration, and other complex and long-horizon manipulation tasks.

Mixed reality-integrated 3D/2D vision mapping for intuitive teleoperation of mobile manipulator

• This work evaluates the impact of the integrated 3D/2D vision and motion mapping schemes on teleoperation efficiency and user experience in a mixed reality (MR) environment. • A significant enhancement in task performance is achieved with the point cloud within an MR environment (MR-3DPC) vision and motion mapping scheme, while minimizing training effort and cognitive workload. • The MR-integrated 3D/2D vision and motion mapping schemes of teleoperation reduced overall task completion times by 34% and 17%. • It is found using point cloud within an MR environment (MR-3DPC) outperforms displaying stereoscopic vision (MR-3DSV) in MR and can enhance users’ stereoscopic perception and task performance. Depth cues are crucial to increase user perception and spatial awareness of the remote environment when remotely guiding complex robotic systems. A mixed reality (MR) integrated 3D/2D vision and motion mapping framework for immersive and intuitive telemanipulation of a complex mobile manipulator is presented. The proposed 3D immersive telerobotic schemes provide the users with depth perception through the merging of multiple 3D/2D views of the remote environment via MR subspace. The mobile manipulator platform consists of a 6°-of-freedom (DOF) industrial manipulator, 3D-printed parallel gripper, and mobile base, which can be controlled by non-skilled operators who are physically separated from the robot working space through a velocity-based imitative motion mapping approach. This work evaluates the impact of depth perception and immersion provided by integrated 3D/2D vision and motion mapping schemes on teleoperation efficiency and user experience in an MR environment. In particular, the MR enhanced systems maintain spatial awareness and perceptual salience of the remote scene in 3D, facilitating intuitive mixed reality human-robot interaction (MR-HRI). This study compared two MR-integrated 3D/2D vision and motion mapping schemes against a typical 2D Baseline visual display method through pick-and-place, assembly, and dexterous manufacturing tasks. The MR-integrated 3D/2D vision and motion mapping schemes of teleoperation reduced overall task completion times by 34% and 17%, compared to the MR-2D Baseline, while minimizing training effort and cognitive workload.

A Planning-and-Control Framework for Aerial Manipulation of Articulated Objects

While the variety of applications for Aerial Manipulators (AMs) has increased over the last years, they are mostly limited to push-and-slide tasks. More complex manipulations of dynamic environments are poorly addressed and still require handcrafted designs of hardware, control, and trajectory planning. In this letter we focus on the active manipulation of articulated objects with AMs. We present a novel planning and control approach that allows the AM to execute complex interaction maneuvers with as little as possible priors given by the operator. Our framework combines sampling-based predictive control to generate pose trajectories with an impedance controller for compliant behaviours, applied to a fully-actuated flying platform. The framework leverages a physics engine to simulate the dynamics of the platform and the environment in order to find optimal motions to execute manipulation tasks. Experiments on two selected examples of pulling open a door and of turning a valve show the feasibility of the proposed approach.

TANDEM: Learning Joint Exploration and Decision Making With Tactile Sensors

Inspired by the human ability to perform complex manipulation in the complete absence of vision (like retrieving an object from a pocket), the robotic manipulation field is motivated to develop new methods for tactile-based object interaction. However, tactile sensing presents the challenge of being an active sensing modality: a touch sensor provides sparse, local data, and must be used in conjunction with effective exploration strategies in order to collect information. In this work, we focus on the process of guiding tactile exploration, and its interplay with task-related decision making. We propose TANDEM (TActile exploration aNd DEcision Making), an architecture to learn efficient exploration strategies in conjunction with decision making. Our approach is based on separate but co-trained modules for exploration and discrimination. We demonstrate this method on a tactile object recognition task, where a robot equipped with a touch sensor must explore and identify an object from a known set based on binary contact signals alone. TANDEM achieves higher accuracy with fewer actions than alternative methods and is also shown to be more robust to sensor noise.

From One Hand to Multiple Hands: Imitation Learning for Dexterous Manipulation From Single-Camera Teleoperation

We propose to perform imitation learning for dexterous manipulation with multi-finger robot hand from human demonstrations, and transfer the policy to the real robot hand. We introduce a novel single-camera teleoperation system to collect the 3D demonstrations efficiently with only an iPad and a computer. One key contribution of our system is that we construct a customized robot hand for each user in the simulator, which is a manipulator resembling the same structure of the operator's hand. It provides an intuitive interface and avoid unstable human-robot hand retargeting for data collection, leading to large-scale and high quality data. Once the data is collected, the customized robot hand trajectories can be converted to different specified robot hands (models that are manufactured) to generate training demonstrations. With imitation learning using our data, we show large improvement over baselines with multiple complex manipulation tasks. Importantly, we show our learned policy is significantly more robust when transferring to the real robot.

What Matters in Language Conditioned Robotic Imitation Learning Over Unstructured Data

A long-standing goal in robotics is to build robots that can perform a wide range of daily tasks from perceptions obtained with their onboard sensors and specified only via natural language. While recently substantial advances have been achieved in language-driven robotics by leveraging end-to-end learning from pixels, there is no clear and well-understood process for making various design choices due to the underlying variation in setups. In this letter, we conduct an extensive study of the most critical challenges in learning language conditioned policies from offline free-form imitation datasets. We further identify architectural and algorithmic techniques that improve performance, such as a hierarchical decomposition of the robot control learning, a multimodal transformer encoder, discrete latent plans and a self-supervised contrastive loss that aligns video and language representations. By combining the results of our investigation with our improved model components, we are able to present a novel approach that significantly outperforms the state of the art on the challenging language conditioned long-horizon robot manipulation CALVIN benchmark. We have open-sourced our implementation to facilitate future research in learning to perform many complex manipulation skills in a row specified with natural language.

The Order & Complexity Toolbox for Aesthetics (OCTA): A systematic approach to study the relations between order, complexity, and aesthetic appreciation.

Do individuals prefer stimuli that are ordered or disordered, simple or complex, or that strike the right balance of order and complexity? Earlier research mainly focused on the separate influence of order and complexity on aesthetic appreciation. When order and complexity were studied in combination, stimulus manipulations were often not parametrically controlled, only rather specific types of order (i.e., balance or symmetry) were usually studied, and/or the multidimensionality of order and complexity was largely ignored. Progress has also been limited by the lack of an easy way to create reproducible and expandible stimulus sets, including both order and complexity manipulations. The Order & Complexity Toolbox for Aesthetics (OCTA), a Python toolbox that is also available as a point-and-click Shiny application, aims to fill this gap. OCTA provides researchers with a free and easy way to create multi-element displays varying qualitatively (i.e., different types) and quantitatively (i.e., different levels) in order and complexity, based on regularity and variety along multiple element features (e.g., shape, size, color, orientation). The standard vector-based output is ideal for experiments on the web and the creation of dynamic interfaces and stimuli. OCTA will not only facilitate reproducible stimulus construction and experimental design in research on order, complexity, and aesthetics. In addition, OCTA can be a very useful tool in any type of research using visual stimuli, or even to create digital art. To illustrate OCTA's potential, we propose several possible applications and diverse questions that can be addressed using OCTA.

Word-final complexity in speech sound intervention: two case studies

ABSTRACT In speech sound intervention, consonant clusters promote generalisation (i.e. improvement in untreated sounds and words), ostensibly due to their relative complexity compared to other phonological targets. However, our understanding of clusters as intervention targets is largely restricted to those in word-initial position (e.g. [fl-], flip). The present study extends available work to consider the effects of word-final consonant cluster targets (e.g. [-ks]). Phonologically complex word-final clusters may be morphologically simple (e.g. mix) or morphologically complex (e.g. packs, inflected with third-person singular) – yet this cross-domain complexity remains an understudied phenomenon. Presently, two case studies provide an initial investigation of word-final cluster intervention targets for children with phonologically based speech sound disorders. Intervention targets for both Anna (3;7 [years;months]) and David (4;1) featured the phonologically complex word-final cluster [-ks], with Anna’s target being morphologically simple and David’s being morphologically complex. Intervention was provided in 45-minute, individual sessions three times per week for a maximum of 18 sessions. Both children demonstrated high target accuracy by intervention’s end. Following intervention, both children demonstrated progress in intelligibility and ability to produce word-final consonant clusters; David further demonstrated generalisation across multiple measures. Results are interpreted with consideration of individual differences and existing research on complexity in phonological intervention. Overall, present findings motivate continued research, as manipulation of word-final complexity allows for emphasis on a context that is relevant for children with speech sound disorders, for peers with difficulties in morphology (including word-final grammatical morphemes) and for the substantial proportion of children demonstrating weaknesses in both domains.

Theoretical analysis of electrical double layer effects on the multiphase flow of Jeffrey fluid through a divergent channel with lubricated walls

A theoretical study of two-phase non-Newtonian fluid with heat transfer is presented in this article. Jeffrey fluid model is used as the base liquid to form a multiphase suspension with the help of gold particles. Heating effects have also been applied on an electro-osmotic two-phase flow through a divergent channel. The lubrication effects have been applied to dampen the skin friction of the opposite walls. An analytical solution is obtained for the nonlinear multiphase fluid flow with heat transfer. Separate expressions for the volumetric flow rate of two-phase flow and pressure gradient are determined via a complex mathematical manipulation. A concise parametric study reveals that slippery walls of the channel have a prominent influence on the momentum of both phases.

Development of an Open Microfluidic Platform for Oocyte One-Stop Vitrification with Cryotop Method.

Oocyte vitrification technology is widely used for assisted reproduction and fertility preservation, which requires precise washing sequences and timings of cryoprotectant agents (CPAs) treatment to relieve the osmotic shock to cells. The gold standard Cryotop method is extensively used in oocyte vitrification and is currently the most commonly used method in reproductive centers. However, the Cryotop method requires precise and complex manual manipulation by an embryologist, whose proficiency directly determines the effect of vitrification. Therefore, in this study, an automatic microfluidic system consisting of a novel open microfluidic chip and a set of automatic devices was established as a standardized operating protocol to facilitate the conventional manual Cryotop method and minimize the osmotic shock applied to the oocyte. The proposed open microfluidic system could smoothly change the CPA concentration around the oocyte during vitrification pretreatment, and transferred the treated oocyte to the Cryotop with a tiny droplet. The system better conformed to the operating habits of embryologists, whereas the integration of commercialized Cryotop facilitates the subsequent freezing and thawing processes. With standardized operating procedures, our system provides consistent treatment effects for each operation, leading to comparable survival rate, mitochondrial membrane potential (MMP) and reactive oxygen species (ROS) level of oocytes to the manual Cryotop operations. The vitrification platform is the first reported microfluidic system integrating the function of cells transfer from the processing chip, which avoids the risk of cell loss or damage in a manual operation and ensures the sufficient cooling rate during liquid nitrogen () freezing. Our study demonstrates significant potential of the automatic microfluidic approach to serve as a facile and universal solution for the vitrification of various precious cells.

Superheterodyne-inspired waveguide-integrated metasurfaces for flexible free-space light manipulation

Abstract Metasurfaces have attracted significant attention in recent years due to their unprecedented light-manipulation abilities. However, most metasurfaces so far have relied on external light excitation, prohibiting them from full on-chip integration. Inspired by the superheterodyne principle in radio communications, here we propose a new waveguide-integrated metasurface architecture capable of converting in-plane guided modes into any desired out-of-plane free-space modes. A theoretical model, verified by simulation and experiment, is developed to provide a deep understanding of the involved physical mechanism and facilitate innovative metasurface designs. The judicious design of baseband signals allows the silicon-based superheterodyne metasurfaces to achieve complex light manipulations, including arbitrary-direction beam deflection and focusing. The proposed superheterodyne metasurface is a marriage of radio communications and photonics. It provides a paradigm shift of metasurface designs and empowers integrated photonic devices with extraordinary free-space interactivity capability, enabling a broad spectrum of applications in communications, remoting sensing, and imaging.

CRISPR/Cpf1-Mediated Multiplex and Large-Fragment Gene Editing in Staphylococcus aureus.

Staphylococcus aureus is a major human pathogen that causes a variety of infections, including life-threatening diseases. Research on S. aureus is constrained by complex and limited genetic manipulation methods. Here, we report a CRISPR/Cpf1-mediated system, pCpfSA, for rapid and versatile genome editing in S. aureus. In direct comparison with the existing CRISPR/Cas9-mediated genome-editing system, the pCpfSA system exhibits enhanced colony-forming units (CFUs) after editing and an expanded targetable range with comparable editing efficiency. Given the precursor crRNA (pre-crRNA) processing activity of Cpf1, the pCpfSA system also allows multiplex gene editing and large-fragment DNA knockout simply by introducing two crRNAs and the corresponding donor templates, which is difficult to achieve using the CRISPR/Cas9 system, thereby greatly expanding the genome editor toolbox for S. aureus.

Origami Microfluidics: A Review of Research Progress and Biomedical Applications

In recent years, origami/paper folding art has been applied to more and more scientific research due to the efficiently producing tiny structures at a low cost; inspired by this, origami microfluidics (the combination of origami/paper folding with paper-based microfluidics) has also been proposed. In origami microfluidic devices, three-dimensional fluid paths can be formed directly by folding the paper with pre-deposited hydrophobic barriers to accomplish complex fluid manipulation. Compared with the layer-by-layer approach, paper folding is more convenient and easier to fabricate three-dimensional microchannels and can meet the increasing requirements of microfluidic end users and improve the functionality of paper-based microdevices. This review is trying to introduce the most current developments of origami paper-based microfluidics, focusing on hydrophobic barrier processing, paper folding, and methods to fix the folded paper. By paper folding, the application prospects of paper-based microfluidics in the biomedical field have been expanded; thus, some biomedical applications of origami microfluidics are also briefly reviewed in this study. Finally, the challenges and perspectives of origami microfluidics were discussed in this review.