堆叠提高了架子上的存储效率,但是缺乏可见性和可访问性使机器人难以揭示和提取目标对象的机械搜索问题。在本文中,我们将横向访问机械搜索问题扩展到带有堆叠项目的架子,并引入了两种新颖的政策 - 堆叠场景(DARSS)和Monte Carlo Tree搜索堆叠场景(MCTSSS)的分配区域减少 - 使用Destacking和恢复行动。 MCTSS通过在每个潜在行动后考虑未来的状态来改善先前的LookAhead政策。在1200次模拟和18个物理试验中进行的实验,配备了刀片和吸力杯,这表明命令和重新攻击动作可以揭示目标对象的模拟成功率为82---100%,而在物理实验中获得了66----100%对于搜索密集包装的架子至关重要。在仿真实验中,这两种策略的表现都优于基线,并获得相似的成功率,但与具有完整状态信息的Oracle政策相比采取了更多步骤。在模拟和物理实验中,DARS在中位数步骤中的表现优于MCTSS,以揭示目标,但是MCTSS在物理实验中的成功率更高,表明对感知噪声的稳健性。请参阅https://sites.google.com/berkeley.edu/stax-ray,以获取补充材料。
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架子通常用于将物体存储在房屋,商店和仓库中。我们制定了最佳架子布置(OSA)的问题,该目标是优化货架上对象的排列,以便在每个对象的访问频率和移动成本下,以获取访问时间。我们提出了一个混合企业计划(MIP)OSA-MIP,表明它在某些条件下找到了OSA的最佳解决方案,并在其一般成本设置中为其次优的解决方案提供了界限。我们在分析上表征了存在的必要且充分的架子密度条件,因此可以在不从架子上删除物体的情况下检索任何对象。来自1,575架模拟货架试验的实验数据和配备有推动刀片和吸入抓握工具的物理fetch机器人的54次试验表明,安排对象可以最佳地将预期的检索成本降低60-80%,以降低预期的搜索和预期的搜索在部分观察到的配置中,成本增加了50-70%,同时将搜索成功率提高到最高2倍。
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以前的工作定义了探索性抓握,其中一个机器人迭代地抓住并丢弃一个未知的复杂多面体物体,以发现一组稳定的掌握对象的每个识别的不同稳定的姿势。最近的工作用来了一个多武装强盗模型,每种姿势一小组候选麦克风;但是,对于具有少数成功Grasps的物体,该组可能不包括最强大的掌握。我们展示了学习高效的掌握装置(腿),这是一种算法,可以通过构建大型有希望的掌握的小型活跃的掌握,并使用学习的信心范围来确定何时何时置信,它可以停止探索对象。实验表明,腿可以比不学习活动集的现有算法更有效地识别高质量的掌握。在仿真实验中,我们测量腿部和基线所识别的最佳掌握的成功概率与真正最强大的掌握的最佳差距。经过3000个探索步骤后,腿部优于14个Dex-Net对手的10个中的基线算法和39 egad的25个!对象。然后,我们开发一个自我监督的掌握系统,机器人探讨了人类干预最小的掌握。 3对象的物理实验表明,腿将从基线收敛到高性能的GRASPS比基线更快。有关补充材料和视频,请参阅\ url {https://sites.google.com/view/legs-exp-grasping}。
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柔软的钳口尖端几乎普遍地与平行钳口机器人夹持器普遍使用,因为它们可以增加接触面积和钳口之间的摩擦和要操纵的物体。然而,符合曲面和刚性物体之间的相互作用是难以模拟的。我们介绍了一种使用增量潜在联系人(IPC)的新型模拟器的IPC-Graspsim - 一个用于计算机图形学的2020年的变形模型 - 这既在抓住期间就模拟了符合JAW提示的动态和变形。 IPC-Graspsim使用一组2,000个物理掌握在16个对手对象中进行评估,其中标准分析模型表现不佳。与分析Quasistatic接触型号(软点接触,REACH,6DFC)和动态掌握模拟器(ISAAC健身房)(具有Flex后端的ISAAC健身房,结果表明IPC-Graspsim更准确地模拟现实世界掌握,增加F1得分9%。所有数据,代码,视频和补充材料都可以在https://sites.google.com/berkeley.edu/ipcgraspsim中获得。
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The body of research on classification of solar panel arrays from aerial imagery is increasing, yet there are still not many public benchmark datasets. This paper introduces two novel benchmark datasets for classifying and localizing solar panel arrays in Denmark: A human annotated dataset for classification and segmentation, as well as a classification dataset acquired using self-reported data from the Danish national building registry. We explore the performance of prior works on the new benchmark dataset, and present results after fine-tuning models using a similar approach as recent works. Furthermore, we train models of newer architectures and provide benchmark baselines to our datasets in several scenarios. We believe the release of these datasets may improve future research in both local and global geospatial domains for identifying and mapping of solar panel arrays from aerial imagery. The data is accessible at https://osf.io/aj539/.
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This is a continuation of our recent paper in which we developed the theory of sequential parametrized motion planning. A sequential parametrized motion planning algorithm produced a motion of the system which is required to visit a prescribed sequence of states, in a certain order, at specified moments of time. In the previous publication we analysed the sequential parametrized topological complexity of the Fadell - Neuwirth fibration which in relevant to the problem of moving multiple robots avoiding collisions with other robots and with obstacles in the Euclidean space. Besides, in the preceeding paper we found the sequential parametrised topological complexity of the Fadell - Neuwirth bundle for the case of the Euclidean space $\Bbb R^d$ of odd dimension as well as the case $d=2$. In the present paper we give the complete answer for an arbitrary $d\ge 2$ even. Moreover, we present an explicit motion planning algorithm for controlling multiple robots in $\Bbb R^d$ having the minimal possible topological complexity; this algorithm is applicable to any number $n$ of robots and any number $m\ge 2$ of obstacles.
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The proliferation of unmanned aircraft systems (UAS) has caused airspace regulation authorities to examine the interoperability of these aircraft with collision avoidance systems initially designed for large transport category aircraft. Limitations in the currently mandated TCAS led the Federal Aviation Administration to commission the development of a new solution, the Airborne Collision Avoidance System X (ACAS X), designed to enable a collision avoidance capability for multiple aircraft platforms, including UAS. While prior research explored using deep reinforcement learning algorithms (DRL) for collision avoidance, DRL did not perform as well as existing solutions. This work explores the benefits of using a DRL collision avoidance system whose parameters are tuned using a surrogate optimizer. We show the use of a surrogate optimizer leads to DRL approach that can increase safety and operational viability and support future capability development for UAS collision avoidance.
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This paper describes important considerations and challenges associated with online reinforcement-learning based waveform selection for target identification in frequency modulated continuous wave (FMCW) automotive radar systems. We present a novel learning approach based on satisficing Thompson sampling, which quickly identifies a waveform expected to yield satisfactory classification performance. We demonstrate through measurement-level simulations that effective waveform selection strategies can be quickly learned, even in cases where the radar must select from a large catalog of candidate waveforms. The radar learns to adaptively select a bandwidth for appropriate resolution and a slow-time unimodular code for interference mitigation in the scene of interest by optimizing an expected classification metric.
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The capture and animation of human hair are two of the major challenges in the creation of realistic avatars for the virtual reality. Both problems are highly challenging, because hair has complex geometry and appearance, as well as exhibits challenging motion. In this paper, we present a two-stage approach that models hair independently from the head to address these challenges in a data-driven manner. The first stage, state compression, learns a low-dimensional latent space of 3D hair states containing motion and appearance, via a novel autoencoder-as-a-tracker strategy. To better disentangle the hair and head in appearance learning, we employ multi-view hair segmentation masks in combination with a differentiable volumetric renderer. The second stage learns a novel hair dynamics model that performs temporal hair transfer based on the discovered latent codes. To enforce higher stability while driving our dynamics model, we employ the 3D point-cloud autoencoder from the compression stage for de-noising of the hair state. Our model outperforms the state of the art in novel view synthesis and is capable of creating novel hair animations without having to rely on hair observations as a driving signal.
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When should an online reinforcement learning-based frequency agile cognitive radar be expected to outperform a rule-based adaptive waveform selection strategy? We seek insight regarding this question by examining a dynamic spectrum access scenario, in which the radar wishes to transmit in the widest unoccupied bandwidth during each pulse repetition interval. Online learning is compared to a fixed rule-based sense-and-avoid strategy. We show that given a simple Markov channel model, the problem can be examined analytically for simple cases via stochastic dominance. Additionally, we show that for more realistic channel assumptions, learning-based approaches demonstrate greater ability to generalize. However, for short time-horizon problems that are well-specified, we find that machine learning approaches may perform poorly due to the inherent limitation of convergence time. We draw conclusions as to when learning-based approaches are expected to be beneficial and provide guidelines for future study.
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