Abstract-This paper presents a overview of the inaugural Amazon Picking Challenge along with a summary of a survey conducted among the 26 participating teams. The challenge goal was to design an autonomous robot to pick items from a warehouse shelf. This task is currently performed by human workers, and there is hope that robots can someday help increase efficiency and throughput while lowering cost. We report on a 28-question survey posed to the teams to learn about each team's background, mechanism design, perception apparatus, planning and control approach. We identify trends in this data, correlate it with each team's success in the competition, and discuss observations and lessons learned based on survey results and the authors' personal experiences during the challenge.Note to Practitioners: Abstract-Perception, motion planning, grasping, and robotic system engineering has reached a level of maturity that makes it possible to explore automating simple warehouse tasks in semi-structured environments that involve high-mix, low-volume picking applications. This survey summarizes lessons learned from the first Amazon Picking Challenge, highlighting mechanism design, perception, and motion planning algorithms, as well as software engineering practices that were most successful in solving a simplified order fulfillment task. While the choice of mechanism mostly affects execution speed, the competition demonstrated the systems challenges of robotics and illustrated the importance of combining reactive control with deliberative planning.

Given careful design and problem formulation, an AI simulation framework can approximate optimal decisions even in complex and uncertain environments. Future work is described that outlines potential lines of research and integration of machine learning algorithms for personalized medicine.

A shift of the angiogenic balance to the proangiogenic state, termed the ''angiogenic switch,'' is a hallmark of cancer progression. Here we devise a strategy for identifying genetic participants of the angiogenic switch based on inverse regulation of genes in human endothelial cells in response to key endogenous pro-and antiangiogenic proteins. This approach reveals a global network pattern for vascular homeostasis connecting known angiogenesisrelated genes with previously unknown signaling components. We also demonstrate that the angiogenic switch is governed by simultaneous regulations of multiple genes organized as transcriptional circuitries. In pancreatic cancer patients, we validate the transcriptome-derived switch of the identified ''angiogenic network:'' The angiogenic state in chronic pancreatitis specimens is intermediate between the normal (angiogenesis off) and neoplastic (angiogenesis on) condition, suggesting that aberrant proangiogenic environment contributes to the increased cancer risk in patients with chronic pancreatitis. In knockout experiments in mice, we show that the targeted removal of a hub node (peroxisome proliferative-activated receptor ␦) of the angiogenic network markedly impairs angiogenesis and tumor growth. Further, in tumor patients, we show that peroxisome proliferative-activated receptor ␦ expression levels are correlated with advanced pathological tumor stage, increased risk for tumor recurrence, and distant metastasis. Our results therefore also may contribute to the rational design of antiangiogenic cancer agents; whereas ''narrow'' targeted cancer drugs may fail to shift the robust angiogenic regulatory network toward antiangiogenesis, the network may be more vulnerable to multiple or broad-spectrum inhibitors or to the targeted removal of the identified angiogenic ''hub'' nodes.angiogenesis ͉ cancer therapy ͉ homeostatic balance ͉ systems biology ͉ pancreatic carcinoma

In this paper we study the quasi-static motion of large legged robots that have many degrees of freedom. While gaited walking may suffice on easy ground, rough and steep terrain requires unique sequences of footsteps and postural adjustments specifically adapted to the terrain's local geometric and physical properties. In this paper we present a planner that computes these motions by combining graph searching to generate a sequence of candidate footfalls with probabilistic sample-based planning to generate continuous motions that reach these footfalls. To improve motion quality, the probabilistic planner derives its sampling strategy from a small set of motion primitives that have been generated offline. The viability of this approach is demonstrated in simulation for the six-legged Lunar vehicle ATHLETE and the humanoid HRP-2 on several example terrains, including one that requires both hand and foot contacts and another that requires rappelling.

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Robots that perform complex manipulation tasks must be able to generate strategies that make and break contact with the object. This requires reasoning in a motion space with a particular multi-modal structure, in which the state contains both a discrete mode (the contact state) and a continuous configuration (the robot and object poses). In this paper we address multi-modal motion planning in the common setting where the state is high-dimensional, and there are a continuous infinity of modes. We present a highly general algorithm, Random-MMP, that repeatedly attempts mode switches sampled at random. A major theoretical result is that Random-MMP is formally reliable and scalable, and its running time depends on certain properties of the multi-modal structure of the problem that are not explicitly dependent on dimensionality. We apply the planner to a manipulation task on the Honda humanoid robot, where the robot is asked to push an object to a desired location on a cluttered table, and the robot is restricted to switch between walking, reaching, and pushing modes. Experiments in simulation and on the real robot demonstrate that Random-MMP solves problem instances that require several carefully chosen pushes in minutes on a PC.

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Purpose: Radiotherapy is used for the treatment of lung cancer, but at the same time induces acute pneumonitis and subsequent pulmonary fibrosis, where TGF-b signaling is considered to play an important role.Experimental Design: We irradiated thoraces of C57BL/6 mice (single dose, 20 Gy) and administered them a novel small-molecule TGF-b receptor I serine/threonine kinase inhibitor (LY2109761) orally for 4 weeks before, during, or after radiation. Noninvasive lung imaging including volume computed tomography (VCT) and MRI was conducted 6, 16, and 20 weeks after irradiation and was correlated to histologic findings. Expression profiling analysis and protein analysis was conducted in human primary fibroblasts.Results: Radiation alone induced acute pulmonary inflammation and lung fibrosis after 16 weeks associated with reduced life span. VCT, MRI, and histology showed that LY2109761 markedly reduced inflammation and pulmonary fibrosis resulting in prolonged survival. Mechanistically, we found that LY2109761 reduced p-SMAD2 and p-SMAD1 expression, and transcriptomics revealed that LY2109761 suppressed expression of genes involved in canonical and noncanonical TGF-b signaling and downstream signaling of bone morphogenetic proteins (BMP). LY2109761 also suppressed radiation-induced inflammatory [e.g., interleukin (IL)-6, IL-7R, IL-8] and proangiogenic genes (e.g., ID1) indicating that LY2109761 achieves its antifibrotic effect by suppressing radiation-induced proinflammatory, proangiogenic, and profibrotic signals.Conclusion: Small-molecule inhibitors of the TGF-b receptor I kinase may offer a promising approach to treat or attenuate radiation-induced lung toxicity or other diseases associated with fibrosis.