We describe the architecture, algorithms, and experiments with HERB, an autonomous mobile manipulator that performs useful manipulation tasks in the home. We present new algorithms for searching for objects, learning to navigate in cluttered dynamic indoor scenes, recognizing and registering objects accurately in high clutter using vision, manipulating doors and other constrained objects using caging grasps, grasp planning and execution in clutter, and manipulation on pose and torque constraint manifolds. We also present numerous severe real-world test results from the integration of these algorithms into a single mobile manipulator.

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Phase contrast time-lapse microscopy is a non-destructive technique that generates large volumes of image-based information to quantify the behaviour of individual cells or cell populations. To guide the development of algorithms for computer-aided cell tracking and analysis, 48 time-lapse image sequences, each spanning approximately 3.5 days, were generated with accompanying ground truths for C2C12 myoblast cells cultured under 4 different media conditions, including with fibroblast growth factor 2 (FGF2), bone morphogenetic protein 2 (BMP2), FGF2 + BMP2, and control (no growth factor). The ground truths generated contain information for tracking at least 3 parent cells and their descendants within these datasets and were validated using a two-tier system of manual curation. This comprehensive, validated dataset will be useful in advancing the development of computer-aided cell tracking algorithms and function as a benchmark, providing an invaluable opportunity to deepen our understanding of individual and population-based cell dynamics for biomedical research.

In the field of lipid research, the measurement of adipocyte size is an important but difficult problem. We describe an imaging-based solution that combines precise investigator control with semiautomated quantitation. By using unfixed live cells, we avoid many complications that arise in trying to isolate individual adipocytes. Instead, we image a small drop of live adipocyte suspension under a microscope, and then quantitate the image using an open-source software tool called FatFind. Since we have developed FatFind on the open-source Diamond distributed search platform, it inherits the scaling, parallelism and remote access attributes of Diamond. This paper reports on the design, implementation, and evaluation of FatFind.

We propose a demonstration of extremely scalable modular robotics algorithms developed as part of the Claytronics Project (http://www-2.cs.cmu.edu/~claytronics/), as well as a demonstration of proof-of-concept prototypes.Our effort envisions multi-million-module robot ensembles able to morph into three-dimensional scenes, eventually with sufficient fidelity so as to convince a human observer the scenes are real. Although this work is potentially revolutionary in the sense that it holds out the possibility of radically altering the relationship between computation, humans, and the physical world, many of the research questions involved are similar in flavor to more mainstream systems research, albeit larger in scale. For instance, as in sensor networks, each robot will incorporate sensing, computation, and communications components. However, unlike most sensor networks each robot will also include mechanisms for actuation and motion. Many of the key challenges in this project involve coordination and communication of sensing and actuation across such large ensembles of independent units. MOTIVATIONThe past six decades have brought tremendous reductions in the physical scale of computing hardware. We envision a similar reduction in the scale of modular robotics, made possible by the extension of present high-volume manufacturing techniques (e.g., as in semiconductor fabrication). Millions of sub-millimeter robot modules each able to emit variable color and intensity light will enable dynamic physical rendering systems, in which a robot ensemble can simulate arbitrary 3D models. Such systems could have many applications beyond robotics, such as telepresence, human-computer interface, and entertainment. DEMO OVERVIEWWe will show some of the software and hardware systems we are developing for Claytronics, including 1) algorithms for selforganizing hierarchical computation and communication aimed at networks with dense, 3D structures and diameters of hundreds to thousands of nodes, and 2) a suite of parallelized algorithms for coordination and planning of movement and sensing operations across both local (tens to hundreds of units) and global scales (thousands to millions of units). In both cases we will show videos of results in simulation. We will also exhibit artifacts from our efforts to build hardware prototypes at a 5cm size, and to substantiate the potential for shrinking these prototype designs to sub-millimeter scale. ACKNOWLEDGMENTS

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