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。

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。

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}。

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。

Dataset distillation has emerged as a prominent technique to improve data efficiency when training machine learning models. It encapsulates the knowledge from a large dataset into a smaller synthetic dataset. A model trained on this smaller distilled dataset can attain comparable performance to a model trained on the original training dataset. However, the existing dataset distillation techniques mainly aim at achieving the best trade-off between resource usage efficiency and model utility. The security risks stemming from them have not been explored. This study performs the first backdoor attack against the models trained on the data distilled by dataset distillation models in the image domain. Concretely, we inject triggers into the synthetic data during the distillation procedure rather than during the model training stage, where all previous attacks are performed. We propose two types of backdoor attacks, namely NAIVEATTACK and DOORPING. NAIVEATTACK simply adds triggers to the raw data at the initial distillation phase, while DOORPING iteratively updates the triggers during the entire distillation procedure. We conduct extensive evaluations on multiple datasets, architectures, and dataset distillation techniques. Empirical evaluation shows that NAIVEATTACK achieves decent attack success rate (ASR) scores in some cases, while DOORPING reaches higher ASR scores (close to 1.0) in all cases. Furthermore, we conduct a comprehensive ablation study to analyze the factors that may affect the attack performance. Finally, we evaluate multiple defense mechanisms against our backdoor attacks and show that our attacks can practically circumvent these defense mechanisms.

We present a dynamic path planning algorithm to navigate an amphibious rotor craft through a concave time-invariant obstacle field while attempting to minimize energy usage. We create a nonlinear quaternion state model that represents the rotor craft dynamics above and below the water. The 6 degree of freedom dynamics used within a layered architecture to generate motion paths for the vehicle to follow and the required control inputs. The rotor craft has a 3 dimensional map of its surroundings that is updated via limited range onboard sensor readings within the current medium (air or water). Path planning is done via PRM and D* Lite.

While the capabilities of autonomous systems have been steadily improving in recent years, these systems still struggle to rapidly explore previously unknown environments without the aid of GPS-assisted navigation. The DARPA Subterranean (SubT) Challenge aimed to fast track the development of autonomous exploration systems by evaluating their performance in real-world underground search-and-rescue scenarios. Subterranean environments present a plethora of challenges for robotic systems, such as limited communications, complex topology, visually-degraded sensing, and harsh terrain. The presented solution enables long-term autonomy with minimal human supervision by combining a powerful and independent single-agent autonomy stack, with higher level mission management operating over a flexible mesh network. The autonomy suite deployed on quadruped and wheeled robots was fully independent, freeing the human supervision to loosely supervise the mission and make high-impact strategic decisions. We also discuss lessons learned from fielding our system at the SubT Final Event, relating to vehicle versatility, system adaptability, and re-configurable communications.

We present Muse, a text-to-image Transformer model that achieves state-of-the-art image generation performance while being significantly more efficient than diffusion or autoregressive models. Muse is trained on a masked modeling task in discrete token space: given the text embedding extracted from a pre-trained large language model (LLM), Muse is trained to predict randomly masked image tokens. Compared to pixel-space diffusion models, such as Imagen and DALL-E 2, Muse is significantly more efficient due to the use of discrete tokens and requiring fewer sampling iterations; compared to autoregressive models, such as Parti, Muse is more efficient due to the use of parallel decoding. The use of a pre-trained LLM enables fine-grained language understanding, translating to high-fidelity image generation and the understanding of visual concepts such as objects, their spatial relationships, pose, cardinality etc. Our 900M parameter model achieves a new SOTA on CC3M, with an FID score of 6.06. The Muse 3B parameter model achieves an FID of 7.88 on zero-shot COCO evaluation, along with a CLIP score of 0.32. Muse also directly enables a number of image editing applications without the need to fine-tune or invert the model: inpainting, outpainting, and mask-free editing. More results are available at https://muse-model.github.io

The visual dimension of cities has been a fundamental subject in urban studies, since the pioneering work of scholars such as Sitte, Lynch, Arnheim, and Jacobs. Several decades later, big data and artificial intelligence (AI) are revolutionizing how people move, sense, and interact with cities. This paper reviews the literature on the appearance and function of cities to illustrate how visual information has been used to understand them. A conceptual framework, Urban Visual Intelligence, is introduced to systematically elaborate on how new image data sources and AI techniques are reshaping the way researchers perceive and measure cities, enabling the study of the physical environment and its interactions with socioeconomic environments at various scales. The paper argues that these new approaches enable researchers to revisit the classic urban theories and themes, and potentially help cities create environments that are more in line with human behaviors and aspirations in the digital age.

Logic Mill is a scalable and openly accessible software system that identifies semantically similar documents within either one domain-specific corpus or multi-domain corpora. It uses advanced Natural Language Processing (NLP) techniques to generate numerical representations of documents. Currently it leverages a large pre-trained language model to generate these document representations. The system focuses on scientific publications and patent documents and contains more than 200 million documents. It is easily accessible via a simple Application Programming Interface (API) or via a web interface. Moreover, it is continuously being updated and can be extended to text corpora from other domains. We see this system as a general-purpose tool for future research applications in the social sciences and other domains.