Jointly Optimizing Placement and Inference for Beacon-based Localization

The ability of robots to estimate their location is crucial for a wide variety of autonomous operations. In settings where GPS is unavailable, measurements of transmissions from fixed beacons provide an effective means of estimating a robot's location as it navigates. The accuracy of such a beacon-based localization system depends both on how beacons are distributed in the environment, and how the robot's location is inferred based on noisy and potentially ambiguous measurements. We propose an approach for making these design decisions automatically and without expert supervision, by explicitly searching for the placement and inference strategies that, together, are optimal for a given environment. Since this search is computationally expensive, our approach encodes beacon placement as a differential neural layer that interfaces with a neural network for inference. This formulation allows us to employ standard techniques for training neural networks to carry out the joint optimization. We evaluate this approach on a variety of environments and settings, and find that it is able to discover designs that enable high localization accuracy.Comment: Appeared at 2017 International Conference on Intelligent Robots and Systems (IROS

N-LIMB: Neural Limb Optimization for Efficient Morphological Design

A robot's ability to complete a task is heavily dependent on its physical design. However, identifying an optimal physical design and its corresponding control policy is inherently challenging. The freedom to choose the number of links, their type, and how they are connected results in a combinatorial design space, and the evaluation of any design in that space requires deriving its optimal controller. In this work, we present N-LIMB, an efficient approach to optimizing the design and control of a robot over large sets of morphologies. Central to our framework is a universal, design-conditioned control policy capable of controlling a diverse sets of designs. This policy greatly improves the sample efficiency of our approach by allowing the transfer of experience across designs and reducing the cost to evaluate new designs. We train this policy to maximize expected return over a distribution of designs, which is simultaneously updated towards higher performing designs under the universal policy. In this way, our approach converges towards a design distribution peaked around high-performing designs and a controller that is effectively fine-tuned for those designs. We demonstrate the potential of our approach on a series of locomotion tasks across varying terrains and show the discovery novel and high-performing design-control pairs.Comment: For code and videos, see https://sites.google.com/ttic.edu/nlim

Jointly Learning to Construct and Control Agents using Deep Reinforcement Learning

The physical design of a robot and the policy that controls its motion are inherently coupled, and should be determined according to the task and environment. In an increasing number of applications, data-driven and learning-based approaches, such as deep reinforcement learning, have proven effective at designing control policies. For most tasks, the only way to evaluate a physical design with respect to such control policies is empirical--i.e., by picking a design and training a control policy for it. Since training these policies is time-consuming, it is computationally infeasible to train separate policies for all possible designs as a means to identify the best one. In this work, we address this limitation by introducing a method that performs simultaneous joint optimization of the physical design and control network. Our approach maintains a distribution over designs and uses reinforcement learning to optimize a control policy to maximize expected reward over the design distribution. We give the controller access to design parameters to allow it to tailor its policy to each design in the distribution. Throughout training, we shift the distribution towards higher-performing designs, eventually converging to a design and control policy that are jointly optimal. We evaluate our approach in the context of legged locomotion, and demonstrate that it discovers novel designs and walking gaits, outperforming baselines in both performance and efficiency

An ovary transcriptome for all maturational stages of the striped bass (Morone saxatilis), a highly advanced perciform fish

<p>Abstract</p> <p>Background</p> <p>The striped bass and its relatives (genus <it>Morone</it>) are important fisheries and aquaculture species native to estuaries and rivers of the Atlantic coast and Gulf of Mexico in North America. To open avenues of gene expression research on reproduction and breeding of striped bass, we generated a collection of expressed sequence tags (ESTs) from a complementary DNA (cDNA) library representative of their ovarian transcriptome.</p> <p>Results</p> <p>Sequences of a total of 230,151 ESTs (51,259,448 bp) were acquired by Roche 454 pyrosequencing of cDNA pooled from ovarian tissues obtained at all stages of oocyte growth, at ovulation (eggs), and during preovulatory atresia. Quality filtering of ESTs allowed assembly of 11,208 high-quality contigs ≥ 100 bp, including 2,984 contigs 500 bp or longer (average length 895 bp). Blastx comparisons revealed 5,482 gene orthologues (E-value < 10^-3), of which 4,120 (36.7% of total contigs) were annotated with Gene Ontology terms (E-value < 10^-6). There were 5,726 remaining unknown unique sequences (51.1% of total contigs). All of the high-quality EST sequences are available in the National Center for Biotechnology Information (NCBI) Short Read Archive (GenBank: <ext-link ext-link-id="SRX007394" ext-link-type="gen">SRX007394</ext-link>). Informative contigs were considered to be abundant if they were assembled from groups of ESTs comprising ≥ 0.15% of the total short read sequences (≥ 345 reads/contig). Approximately 52.5% of these abundant contigs were predicted to have predominant ovary expression through digital differential display <it>in silico </it>comparisons to zebrafish (<it>Danio rerio</it>) UniGene orthologues. Over 1,300 Gene Ontology terms from Biological Process classes of Reproduction, Reproductive process, and Developmental process were assigned to this collection of annotated contigs.</p> <p>Conclusions</p> <p>This first large reference sequence database available for the ecologically and economically important temperate basses (genus <it>Morone</it>) provides a foundation for gene expression studies in these species. The predicted predominance of ovary gene expression and assignment of directly relevant Gene Ontology classes suggests a powerful utility of this dataset for analysis of ovarian gene expression related to fundamental questions of oogenesis. Additionally, a high definition Agilent 60-mer oligo ovary 'UniClone' microarray with 8 × 15,000 probe format has been designed based on this striped bass transcriptome (eArray Group: Striper Group, Design ID: 029004).</p

Aortic valve replacement in patients aged 50 to 70 years: Improved outcome with mechanical versus biologic prostheses

ObjectiveImproved durability of bioprostheses has led some surgeons to recommend biologic rather than mechanical prostheses for patients younger than 65 years. We compared late results of contemporary bioprostheses and bileaflet mechanical prostheses in patients who underwent aortic valve replacement between 50 and 70 years old.MethodsIn this retrospective study, patients received either St Jude bileaflet valves or Carpentier–Edwards bioprostheses. Operations were performed between January 1991 and December 2000, and groups were matched one-to-one according to age, sex, need for coronary artery bypass grafting, and valve size.ResultsFour hundred forty patients were matched, and follow-up was 92% complete, with median durations of 9.1 years for patients who received mechanical valves and 6.2 years for patients who received bioprostheses. The 5- and 10-year unadjusted survivals were 87% and 68% for mechanical valves and 72% and 50% for bioprostheses, respectively (P < .01). Freedoms from reoperation at 10 years were 98% for mechanical valves and 91% for bioprostheses (P = .06). Rates of late stroke or other embolic events and of endocarditis were similar between groups. Hemorrhagic complications necessitating hospitalization occurred in 15% of patients with mechanical valves and 7% of patients with bioprostheses (P = .01). Notably, 19% of patients with bioprostheses were receiving warfarin sodium at last follow-up. After adjustment for unmatched variables, including diabetes, renal failure, lung disease, New York Heart Association functional class, ejection fraction, and stroke, the use of a mechanical valve was protective against late mortality (hazard ratio 0.46, P < .01).ConclusionIn this study, patients aged 50 to 70 years who underwent aortic valve replacement with mechanical valves had a survival advantage relative to matched patients who received bioprostheses. These findings question recommendations of bioprostheses for younger patients and suggest that a randomized trial may be warranted