MZnet : mail service for personal micro-computer systems

Traditional computer mail systems involve a co-resident User Agent (UA) and Mail Transfer System (MTS) on a time-shared host computer which may be connected to other hosts ina network, with new mail posted or delivered directly through co-resident mail-slot programs. To introduce personal micro-computers (PCs) into this environment requires modification of the traditional mail system architecture. To this end, the MZnet project uses a split-slot model, placing UA programs on the PCs while leaving MTA programs on a mail relay host which can provide authentication and buffering. The split-slot arrangement might be viewed as a new protocol level which operates somewhere between the currently defined MTS-MTS and UA-UA levels

3D Object Discovery and Modeling Using Single RGB-D Images Containing Multiple Object Instances

Unsupervised object modeling is important in robotics, especially for handling a large set of objects. We present a method for unsupervised 3D object discovery, reconstruction, and localization that exploits multiple instances of an identical object contained in a single RGB-D image. The proposed method does not rely on segmentation, scene knowledge, or user input, and thus is easily scalable. Our method aims to find recurrent patterns in a single RGB-D image by utilizing appearance and geometry of the salient regions. We extract keypoints and match them in pairs based on their descriptors. We then generate triplets of the keypoints matching with each other using several geometric criteria to minimize false matches. The relative poses of the matched triplets are computed and clustered to discover sets of triplet pairs with similar relative poses. Triplets belonging to the same set are likely to belong to the same object and are used to construct an initial object model. Detection of remaining instances with the initial object model using RANSAC allows to further expand and refine the model. The automatically generated object models are both compact and descriptive. We show quantitative and qualitative results on RGB-D images with various objects including some from the Amazon Picking Challenge. We also demonstrate the use of our method in an object picking scenario with a robotic arm

Motion Priority Optimization Framework towards Automated and Teleoperated Robot Cooperation in Industrial Recovery Scenarios

In this study, we present an optimization framework for efficient motion priority design between automated and teleoperated robots in an industrial recovery scenario. Although robots have recently become increasingly common in industrial sites, there are still challenges in achieving human-robot collaboration/cooperation (HRC), where human workers and robots are engaged in collaborative and cooperative tasks in a shared workspace. For example, the corresponding factory cell must be suspended for safety when an industrial robot drops an assembling part in the workspace. After that, a human worker is allowed to enter the robot workspace to address the robot recovery. This process causes non-continuous manufacturing, which leads to a productivity reduction. Recently, robotic teleoperation technology has emerged as a promising solution to enable people to perform tasks remotely and safely. This technology can be used in the recovery process in manufacturing failure scenarios. Our proposition involves the design of an appropriate priority function that aids in collision avoidance between the manufacturing and recovery robots and facilitates continuous processes with minimal production loss within an acceptable risk level. This paper presents a framework, including an HRC simulator and an optimization formulation, for finding optimal parameters of the priority function. Through quantitative and qualitative experiments, we address the proof of our novel concept and demonstrate its feasibility

Transfer of Fas (CD95) protein from the cell surface to the surface of polystyrene beads coated with anti-Fas antibody clone CH-11

Mouse monoclonal anti-Fas (CD95) antibody clone CH-11 has been widely used in research on apoptosis. CH-11 has the ability to bind to Fas protein on cell surface and induce apoptosis. Here, we used polystyrene beads coated with CH-11 to investigate the role of lipid rafts in Fas-mediated apoptosis in SKW6.4 cells. Unexpectedly, by treatment of the cells with CH-11-coated beads Fas protein was detached from cell surface and transferred to the surface of CH-11-coated beads. Western blot analysis showed that Fas protein containing both extracellular and intracellular domains was attached to the beads. Fas protein was not transferred from the cells to the surface of the beads coated with other anti-Fas antibodies or Fas ligand. Similar phenomenon was observed in Jurkat T cells. Furthermore, CH-11-induced apoptosis was suppressed by pretreatment with CH-11-coated beads in Jurkat cells. These results suggest that CH-11 might possess distinct properties on Fas protein compared with other anti-Fas antibodies or Fas ligand, and also suggest that caution should be needed to use polystyrene beads coated with antibodies such as CH-11

Learning to Dexterously Pick or Separate Tangled-Prone Objects for Industrial Bin Picking

Industrial bin picking for tangled-prone objects requires the robot to either pick up untangled objects or perform separation manipulation when the bin contains no isolated objects. The robot must be able to flexibly perform appropriate actions based on the current observation. It is challenging due to high occlusion in the clutter, elusive entanglement phenomena, and the need for skilled manipulation planning. In this paper, we propose an autonomous, effective and general approach for picking up tangled-prone objects for industrial bin picking. First, we learn PickNet - a network that maps the visual observation to pixel-wise possibilities of picking isolated objects or separating tangled objects and infers the corresponding grasp. Then, we propose two effective separation strategies: Dropping the entangled objects into a buffer bin to reduce the degree of entanglement; Pulling to separate the entangled objects in the buffer bin planned by PullNet - a network that predicts position and direction for pulling from visual input. To efficiently collect data for training PickNet and PullNet, we embrace the self-supervised learning paradigm using an algorithmic supervisor in a physics simulator. Real-world experiments show that our policy can dexterously pick up tangled-prone objects with success rates of 90%. We further demonstrate the generalization of our policy by picking a set of unseen objects. Supplementary material, code, and videos can be found at https://xinyiz0931.github.io/tangle.Comment: 8 page