在本文中，我们为LIMM介绍了一个运动计划者，该计划者是一个模块化的多模式包装输送平台。单个limms单元是一个机器人，它可以作为手臂或腿部操作，具体取决于它的附加方式和内容，例如，当操纵器固定在送货车内的墙壁上时，或将4个附加在盒子附加到盒子的墙壁上时。当每个限制的角色都可以扮演截然不同的角色时，在多个lim上进行协调，很快就会变得复杂。对于这样一个计划问题，我们首先构成了必要的逻辑和约束。然后，该公式将用于技能探索，并可以在精炼后在硬件上实现。为了解决此优化问题，我们使用乘数的交替方向方法（ADMM）。在各种情况下，对拟议的规划师进行了实验，该计划显示了LIMMS进入不同模式或组合的能力，以实现其移动运输箱的目标。

尽管腿部机器人的运动计划表现出了巨大的成功，但具有灵活的多指抓握的腿部机器人的运动计划尚未成熟。我们提出了一个有效的运动计划框架，用于同时解决运动（例如，质心动力学），抓地力（例如，贴片接触）和触点（例如步态）问题。为了加速计划过程，我们建议基于乘数的交替方向方法（ADMM）提出分布式优化框架，以求解原始的大型混合构成非整数非线性编程（MINLP）。最终的框架使用混合构成二次编程（MIQP）来求解联系人和非线性编程（NLP）来求解非线性动力学，这些动力学在计算方面更可行，对参数较不敏感。此外，我们通过微蜘蛛抓手从极限表面明确执行补丁接触约束。我们在硬件实验中演示了我们提出的框架，这表明多限制机器人能够实现各种动作，包括在斜坡角度45 {\ deg}的情况下进行较短的计划时间。

本文介绍了Scalucs，这是一种四足动物，该机器人在地上，悬垂和天花板上爬上攀爬，并在地面上爬行。 Scaleer是最早的自由度四束机器人之一，可以在地球的重力下自由攀爬，也是地面上最有效的四足动物之一。在其他最先进的登山者专门攀登自己的地方，Scaleer承诺使用有效载荷\ Textit {和}地面运动实践自由攀爬，这实现了真正的多功能移动性。新的攀登步态滑冰步态通过利用缩放器的身体连锁机制来增加有效载荷。 Scaleer在地面上达到了最大归一化的运动速度，即$ 1.87 $ /s，$ 0.56 $ m /s，$ 1.2 $ /min，或$ 0.42 $ m /min /min的岩石墙攀爬。有效载荷能力达到地面上缩放器重量的233美元，垂直墙上的$ 35 $％。我们的山羊抓手是一种机械适应的两指抓手，成功地抓住了凸凸和非凸的对象，并支持缩放器。

我们展示了一个具有自动调整的入口控制器，该控制器可用于单个和多点接触机器人（例如，带有点脚或多指握把的腿部机器人）。控制器的目标是跟踪每个接触点的扳手轮廓，同时考虑旋转摩擦引起的额外扭矩。我们的接收控制器在在线操作期间具有自适应性，该方法通过自动调整方法调整了控制器的收益，同时遵循几个培训目标，以促进控制器稳定性，例如尽可能接近跟踪扳手配置文件，以确保控制输出在实力之内限制最小化滑移并避免运动学奇异性。我们使用多限制的攀登机器人来证明控制器在硬件上的鲁棒性，用于操纵和运动任务。

Many e-commerce marketplaces offer their users fast delivery options for free to meet the increasing needs of users, imposing an excessive burden on city logistics. Therefore, understanding e-commerce users' preference for delivery options is a key to designing logistics policies. To this end, this study designs a stated choice survey in which respondents are faced with choice tasks among different delivery options and time slots, which was completed by 4,062 users from the three major metropolitan areas in Japan. To analyze the data, mixed logit models capturing taste heterogeneity as well as flexible substitution patterns have been estimated. The model estimation results indicate that delivery attributes including fee, time, and time slot size are significant determinants of the delivery option choices. Associations between users' preferences and socio-demographic characteristics, such as age, gender, teleworking frequency and the presence of a delivery box, were also suggested. Moreover, we analyzed two willingness-to-pay measures for delivery, namely, the value of delivery time savings (VODT) and the value of time slot shortening (VOTS), and applied a non-semiparametric approach to estimate their distributions in a data-oriented manner. Although VODT has a large heterogeneity among respondents, the estimated median VODT is 25.6 JPY/day, implying that more than half of the respondents would wait an additional day if the delivery fee were increased by only 26 JPY, that is, they do not necessarily need a fast delivery option but often request it when cheap or almost free. Moreover, VOTS was found to be low, distributed with the median of 5.0 JPY/hour; that is, users do not highly value the reduction in time slot size in monetary terms. These findings on e-commerce users' preferences can help in designing levels of service for last-mile delivery to significantly improve its efficiency.

In recent years, various service robots have been introduced in stores as recommendation systems. Previous studies attempted to increase the influence of these robots by improving their social acceptance and trust. However, when such service robots recommend a product to customers in real environments, the effect on the customers is influenced not only by the robot itself, but also by the social influence of the surrounding people such as store clerks. Therefore, leveraging the social influence of the clerks may increase the influence of the robots on the customers. Hence, we compared the influence of robots with and without collaborative customer service between the robots and clerks in two bakery stores. The experimental results showed that collaborative customer service increased the purchase rate of the recommended bread and improved the impression regarding the robot and store experience of the customers. Because the results also showed that the workload required for the clerks to collaborate with the robot was not high, this study suggests that all stores with service robots may show high effectiveness in introducing collaborative customer service.

We introduce \textsc{PoliteRewrite} -- a dataset for polite language rewrite which is a novel sentence rewrite task. Compared with previous text style transfer tasks that can be mostly addressed by slight token- or phrase-level edits, polite language rewrite requires deep understanding and extensive sentence-level edits over an offensive and impolite sentence to deliver the same message euphemistically and politely, which is more challenging -- not only for NLP models but also for human annotators to rewrite with effort. To alleviate the human effort for efficient annotation, we first propose a novel annotation paradigm by a collaboration of human annotators and GPT-3.5 to annotate \textsc{PoliteRewrite}. The released dataset has 10K polite sentence rewrites annotated collaboratively by GPT-3.5 and human, which can be used as gold standard for training, validation and test; and 100K high-quality polite sentence rewrites by GPT-3.5 without human review. We wish this work (The dataset (10K+100K) will be released soon) could contribute to the research on more challenging sentence rewrite, and provoke more thought in future on resource annotation paradigm with the help of the large-scaled pretrained models.

Recent years have seen progress beyond domain-specific sound separation for speech or music towards universal sound separation for arbitrary sounds. Prior work on universal sound separation has investigated separating a target sound out of an audio mixture given a text query. Such text-queried sound separation systems provide a natural and scalable interface for specifying arbitrary target sounds. However, supervised text-queried sound separation systems require costly labeled audio-text pairs for training. Moreover, the audio provided in existing datasets is often recorded in a controlled environment, causing a considerable generalization gap to noisy audio in the wild. In this work, we aim to approach text-queried universal sound separation by using only unlabeled data. We propose to leverage the visual modality as a bridge to learn the desired audio-textual correspondence. The proposed CLIPSep model first encodes the input query into a query vector using the contrastive language-image pretraining (CLIP) model, and the query vector is then used to condition an audio separation model to separate out the target sound. While the model is trained on image-audio pairs extracted from unlabeled videos, at test time we can instead query the model with text inputs in a zero-shot setting, thanks to the joint language-image embedding learned by the CLIP model. Further, videos in the wild often contain off-screen sounds and background noise that may hinder the model from learning the desired audio-textual correspondence. To address this problem, we further propose an approach called noise invariant training for training a query-based sound separation model on noisy data. Experimental results show that the proposed models successfully learn text-queried universal sound separation using only noisy unlabeled videos, even achieving competitive performance against a supervised model in some settings.

Removing reverb from reverberant music is a necessary technique to clean up audio for downstream music manipulations. Reverberation of music contains two categories, natural reverb, and artificial reverb. Artificial reverb has a wider diversity than natural reverb due to its various parameter setups and reverberation types. However, recent supervised dereverberation methods may fail because they rely on sufficiently diverse and numerous pairs of reverberant observations and retrieved data for training in order to be generalizable to unseen observations during inference. To resolve these problems, we propose an unsupervised method that can remove a general kind of artificial reverb for music without requiring pairs of data for training. The proposed method is based on diffusion models, where it initializes the unknown reverberation operator with a conventional signal processing technique and simultaneously refines the estimate with the help of diffusion models. We show through objective and perceptual evaluations that our method outperforms the current leading vocal dereverberation benchmarks.

Mutation-based fuzzing has become one of the most common vulnerability discovery solutions over the last decade. Fuzzing can be optimized when targeting specific programs, and given that, some studies have employed online optimization methods to do it automatically, i.e., tuning fuzzers for any given program in a program-agnostic manner. However, previous studies have neither fully explored mutation schemes suitable for online optimization methods, nor online optimization methods suitable for mutation schemes. In this study, we propose an optimization framework called SLOPT that encompasses both a bandit-friendly mutation scheme and mutation-scheme-friendly bandit algorithms. The advantage of SLOPT is that it can generally be incorporated into existing fuzzers, such as AFL and Honggfuzz. As a proof of concept, we implemented SLOPT-AFL++ by integrating SLOPT into AFL++ and showed that the program-agnostic optimization delivered by SLOPT enabled SLOPT-AFL++ to achieve higher code coverage than AFL++ in all of ten real-world FuzzBench programs. Moreover, we ran SLOPT-AFL++ against several real-world programs from OSS-Fuzz and successfully identified three previously unknown vulnerabilities, even though these programs have been fuzzed by AFL++ for a considerable number of CPU days on OSS-Fuzz.