Data-Driven Grasp Synthesis - A Survey

We review the work on data-driven grasp synthesis and the methodologies for sampling and ranking candidate grasps. We divide the approaches into three groups based on whether they synthesize grasps for known, familiar or unknown objects. This structure allows us to identify common object representations and perceptual processes that facilitate the employed data-driven grasp synthesis technique. In the case of known objects, we concentrate on the approaches that are based on object recognition and pose estimation. In the case of familiar objects, the techniques use some form of a similarity matching to a set of previously encountered objects. Finally for the approaches dealing with unknown objects, the core part is the extraction of specific features that are indicative of good grasps. Our survey provides an overview of the different methodologies and discusses open problems in the area of robot grasping. We also draw a parallel to the classical approaches that rely on analytic formulations.Comment: 20 pages, 30 Figures, submitted to IEEE Transactions on Robotic

Data-Driven Grasp Synthesis—A Survey

We review the work on data-driven grasp synthesis and the methodologies for sampling and ranking candidate grasps. We divide the approaches into three groups based on whether they synthesize grasps for known, familiar, or unknown objects. This structure allows us to identify common object representations and perceptual processes that facilitate the employed data-driven grasp synthesis technique. In the case of known objects, we concentrate on the approaches that are based on object recognition and pose estimation. In the case of familiar objects, the techniques use some form of a similarity matching to a set of previously encountered objects. Finally, for the approaches dealing with unknown objects, the core part is the extraction of specific features that are indicative of good grasps. Our survey provides an overview of the different methodologies and discusses open problems in the area of robot grasping. We also draw a parallel to the classical approaches that rely on analytic formulations

A learning approach to the FOM problem

Hogan recently provided an heuristic technique called family of modes (FOM) to solve model predictive control (MPC) problems under hybrid constraints and underactuation. The goal of this study is to further develop this new method and to expand its usage in the robotics manipulation community. With that objective in mind, we address some of the method's weaknesses, we provide comparison tools to try to compare the method with traditional MPC solving techniques and we provide a simple and systematic technique to set-up the method's parameters. We conclude the study by presenting our the future lines of research, which consist in generalizing the method for more complex systems and testing it's robustness.Outgoin

Capture and generalisation of close interaction with objects

Robust manipulation capture and retargeting has been a longstanding goal in both the fields of animation and robotics. In this thesis I describe a new approach to capture both the geometry and motion of interactions with objects, dealing with the problems of occlusion by the use of magnetic systems, and performing the reconstruction of the geometry by an RGB-D sensor alongside visual markers. This ‘interaction capture’ allows the scene to be described in terms of the spatial relationships between the character and the object using novel topological representations such as the Electric Parameters, which parametrise the outer space of an object using properties of the surface of the object. I describe the properties of these representations for motion generalisation and discuss how they can be applied to the problems of human-like motion generation and programming by demonstration. These generalised interactions are shown to be valid by demonstration of retargeting grasping and manipulation to robots with dissimilar kinematics and morphology using only local, gradient-based planning

Grasping for the Task:Human Principles for Robot Hands

The significant advances made in the design and construction of anthropomorphic robot hands, endow them with prehensile abilities reaching that of humans. However, using these powerful hands with the same level of expertise that humans display is a big challenge for robots. Traditional approaches use finger-tip (precision) or enveloping (power) methods to generate the best force closure grasps. However, this ignores the variety of prehensile postures available to the hand and also the larger context of arm action. This thesis explores a paradigm for grasp formation based on generating oppositional pressure within the hand, which has been proposed as a functional basis for grasping in humans (MacKenzie and Iberall, 1994). A set of opposition primitives encapsulates the hand's ability to generate oppositional forces. The oppositional intention encoded in a primitive serves as a guide to match the hand to the object, quantify its functional ability and relate this to the arm. In this thesis we leverage the properties of opposition primitives to both interpret grasps formed by humans and to construct grasps for a robot considering the larger context of arm action. In the first part of the thesis we examine the hypothesis that hand representation schemes based on opposition are correlated with hand function. We propose hand-parameters describing oppositional intention and compare these with commonly used methods such as joint angles, joint synergies and shape features. We expect that opposition-based parameterizations, which take an interaction-based perspective of a grasp, are able to discriminate between grasps that are similar in shape but different in functional intent. We test this hypothesis using qualitative assessment of precision and power capabilities found in existing grasp taxonomies. The next part of the thesis presents a general method to recognize oppositional intention manifested in human grasp demonstrations. A data glove instrumented with tactile sensors is used to provide the raw information regarding hand configuration and interaction force. For a grasp combining several cooperating oppositional intentions, hand surfaces can be simultaneously involved in multiple oppositional roles. We characterize the low-level interactions between different surfaces of the hand based on captured interaction force and reconstructed hand surface geometry. This is subsequently used to separate out and prioritize multiple and possibly overlapping oppositional intentions present in the demonstrated grasp. We evaluate our method on several human subjects across a wide range of hand functions. The last part of the thesis applies the properties encoded in opposition primitives to optimize task performance of the arm, for tasks where the arm assumes the dominant role. For these tasks, choosing the strongest power grasp available (from a force-closure sense) may constrain the arm to a sub-optimal configuration. Weaker grasp components impose fewer constraints on the hand, and can therefore explore a wider region of the object relative pose space. We take advantage of this to find the good arm configurations from a task perspective. The final hand-arm configuration is obtained by trading of overall robustness in the grasp with ability of the arm to perform the task. We validate our approach, using the tasks of cutting, hammering, screw-driving and opening a bottle-cap, for both human and robotic hand-arm systems

Applications of Optical Control of Oligonucleotide and Protein Function

Optical regulation using light as an external trigger was applied to the control of biological processes with high spatio-temporal resolution. Photoremovable caging groups were site-specifically incorporated onto oligonucleotides and proteins to optically regulate their function in biological environments, typically for the photochemical control of gene expression. These caging group modifications enabled both OFF → ON and ON → OFF optochemical switches for important chemical biology tools. Oligonucleotides containing caging group modifications were synthesized to regulate nucleic acid function with light. Specifically, photocaged triplex-forming oligonucleotides were developed to optochemically control transcription in cell culture. Light-activated antagomirs were designed for the optical inhibition of miR-21 and miR-122 function in the regulation of endogenous microRNA activity. This technology was then applied to the study of miR-22 and miR-124 function in cortical neuron migration during cerebral corticogenesis. Splice-switching oligonucleotides were engineered to optically control mRNA splicing pathways in both human cells and zebrafish. The optical control of plasmid-based gene expression was demonstrated with a caged promoter, and applied to the photochemical activation of transcription in a live animal model. The caging of oligonucleotides was also applied to DNA computation in the production of optically controlled logic gates and amplification cycles, providing spatio-temporal control over hybridization cascades to add new functionality to DNA computation modules. These studies in DNA computation led to the development of novel biosensors for logic gate-based detection of specific micro RNA signatures in live cells. In addition, proteins were optically controlled through the site-specific installation of caging groups on amino acid side chains that are essential for protein function using unnatural amino acid mutagenesis in mammalian cells with an expanded genetic code. A caged lysine analogue was incorporated into T7 RNA polymerase to photochemically regulate transcription in the development of a light-activated synthetic gene network and light-triggered RNA interference. A light-activated Cas9 endonuclease was engineered through the installation of a caged lysine analogue to optically control CRISPR/Cas9 editing of both exogenous and endogenous genes. Lastly, a system for the incorporation of unnatural amino acids in zebrafish was studied in efforts to produce the first vertebrate species with an expanded genetic code

Aerospace Medicine and Biology: A continuing bibliography with indexes

This bibliography lists 223 reports, articles, and other documents introduced into the NASA scientific and technical information system in September 1975

Alpha-Synuclein: Insight into the Hallmark of Parkinson\u27s Disease as a Target for Quantitative Molecular Diagnostics and Therapeutics

Parkinson’s disease (PD) is the second-most common neurodegenerative disease after Alzheimer’s disease. With 500,000 individuals currently living with Parkinson’s and nearly 60,000 new cases diagnosed each year, this disease causes significant financial burden on the healthcare system - amassing to annual expenditures totaling 200 billion dollars; predicted to increase through 2050. The disease phenotype is characterized by a combination of a resting tremor, bradykinesia, muscular rigidity, and depression due to dopaminergic neuronal death in the midbrain. The cause of the neurotoxicity has been largely discussed, with strong evidence suggesting that the protein, alpha-Synuclein, is a key factor. Under native conditions, alpha-Synuclein can be found localized at synaptic terminals where it is hypothesized to be involved in vesicle trafficking and recycling. However, its biochemical profile reveals a hydrophobic region that, once subjected to insult, initiates an aggregation cascade. Oligomeric species—products of the aggregation cascade—demonstrate marked neurotoxicity in dopaminergic neurons and illustrate migratory potential to neighboring healthy neurons, thereby contributing to progressive neurodegeneration. The current golden standard for PD diagnostics is a highly qualitative system involving a process-by-elimination with accuracy that is contingent upon physician experience. This, and a lack of standardized clinical testing procedures, lends to a 25% misdiagnosis rate. Even under circumstances of an accurate PD diagnosis, the only treatment options are pharmacologics that have a wide range of adverse side effects and ultimately contribute to systemic metabolic dysfunction. Thus, the research presented in this thesis seeks to overcome these current challenges by providing (1) a quantitative diagnostic platform and (2) a biomolecular therapeutic, towards oligomeric alpha-Synuclein. Aim 1: serves as a proof-of-concept for the use of catalytic nucleic acid moieties, deoxyribozymes and aptamers, to quantify alpha-Synuclein in a novel manner and explore the ability to detect oligomeric cytotoxic species. The cost-effective nature of these sensors allows for continued optimization. Aim 2: serves to establish a potential therapy that can abrogate alpha-synuclein oligomerization and toxicity through use of a modified Protein Disulfide Isomerase (PDI) peptide when introduced to live cells treated to simulate pre-parkinsonian pathology