人类语言学习者暴露于信息丰富的上下文敏感语言，但要大量的原始感觉数据。通过社会语言的使用和彩排和实践的内部过程，语言学习者能够建立高级的语义表示，以解释他们的看法。在这里，我们从人类中的“内在语音”过程中汲取灵感（Vygotsky，1934年），以更好地理解代理内语言在体现行为中的作用。首先，我们正式地将代理语音作为半监督问题，并开发了两种算法，这些算法能够以很少的标记语言数据进行视觉接地字幕。然后，我们通过实验计算不同量的标记数据的缩放曲线，并将数据效率与监督学习基线进行比较。最后，我们将演讲内部的语音纳入3D虚拟世界中运行的体现的移动操纵剂代理，并表明，只需多达150个附加图像标题，代理语音就可以操纵和回答有关的问题。一个没有任何相关任务经验的新对象（零射）。综上所述，我们的实验表明，对代理内部的语音进行建模有效，可以使体现的代理有效地学习新任务，而无需直接互动经验。

创建可以自然与人类互动的代理是人工智能（AI）研究中的共同目标。但是，评估这些互动是具有挑战性的：收集在线人类代理相互作用缓慢而昂贵，但更快的代理指标通常与交互式评估相关。在本文中，我们评估了这些现有评估指标的优点，并提出了一种新颖的评估方法，称为标准化测试套件（STS）。 STS使用从真实人类交互数据中挖掘出的行为方案。代理商请参阅重播方案上下文，接收指令，然后将控制权控制以脱机完成交互。记录这些代理的延续并将其发送给人类注释者以将其标记为成功或失败，并且根据其成功的连续性比例对代理进行排名。最终的ST是自然主义相互作用的快速，控制，可解释的和代表的。总的来说，STS巩固了我们许多标准评估指标中所需的许多值，从而使我们能够加速研究进展，以生产可以自然与人类互动的代理。可以在https://youtu.be/yr1tnggorgq上找到视频。

来自科幻小说的普通愿景是机器人将有一天居住在我们的物理空间中，感知世界，才能协助我们的物理劳动力，并通过自然语言与我们沟通。在这里，我们研究如何使用虚拟环境的简化设计如何与人类自然交互的人工代理。我们表明，与自我监督学习的模拟世界中的人类交互的模仿学习足以产生我们称之为MIA的多模式互动剂，这成功与非对抗人类互动75％的时间。我们进一步确定了提高性能的架构和算法技术，例如分层动作选择。完全，我们的结果表明，模仿多模态，实时人类行为可以提供具有丰富的行为的富含性的令人生意的和令人惊讶的有效手段，然后可以为特定目的进行微调，从而铺设基础用于培训互动机器人或数字助理的能力。可以在https://youtu.be/zfgrif7my找到MIA的行为的视频

It is well known that conservative mechanical systems exhibit local oscillatory behaviours due to their elastic and gravitational potentials, which completely characterise these periodic motions together with the inertial properties of the system. The classification of these periodic behaviours and their geometric characterisation are in an on-going secular debate, which recently led to the so-called eigenmanifold theory. The eigenmanifold characterises nonlinear oscillations as a generalisation of linear eigenspaces. With the motivation of performing periodic tasks efficiently, we use tools coming from this theory to construct an optimization problem aimed at inducing desired closed-loop oscillations through a state feedback law. We solve the constructed optimization problem via gradient-descent methods involving neural networks. Extensive simulations show the validity of the approach.

Our aim is to build autonomous agents that can solve tasks in environments like Minecraft. To do so, we used an imitation learning-based approach. We formulate our control problem as a search problem over a dataset of experts' demonstrations, where the agent copies actions from a similar demonstration trajectory of image-action pairs. We perform a proximity search over the BASALT MineRL-dataset in the latent representation of a Video PreTraining model. The agent copies the actions from the expert trajectory as long as the distance between the state representations of the agent and the selected expert trajectory from the dataset do not diverge. Then the proximity search is repeated. Our approach can effectively recover meaningful demonstration trajectories and show human-like behavior of an agent in the Minecraft environment.

Object instance segmentation is a key challenge for indoor robots navigating cluttered environments with many small objects. Limitations in 3D sensing capabilities often make it difficult to detect every possible object. While deep learning approaches may be effective for this problem, manually annotating 3D data for supervised learning is time-consuming. In this work, we explore zero-shot instance segmentation (ZSIS) from RGB-D data to identify unseen objects in a semantic category-agnostic manner. We introduce a zero-shot split for Tabletop Objects Dataset (TOD-Z) to enable this study and present a method that uses annotated objects to learn the ``objectness'' of pixels and generalize to unseen object categories in cluttered indoor environments. Our method, SupeRGB-D, groups pixels into small patches based on geometric cues and learns to merge the patches in a deep agglomerative clustering fashion. SupeRGB-D outperforms existing baselines on unseen objects while achieving similar performance on seen objects. Additionally, it is extremely lightweight (0.4 MB memory requirement) and suitable for mobile and robotic applications. The dataset split and code will be made publicly available upon acceptance.

Increasingly taking place in online spaces, modern political conversations are typically perceived to be unproductively affirming -- siloed in so called ``echo chambers'' of exclusively like-minded discussants. Yet, to date we lack sufficient means to measure viewpoint diversity in conversations. To this end, in this paper, we operationalize two viewpoint metrics proposed for recommender systems and adapt them to the context of social media conversations. This is the first study to apply these two metrics (Representation and Fragmentation) to real world data and to consider the implications for online conversations specifically. We apply these measures to two topics -- daylight savings time (DST), which serves as a control, and the more politically polarized topic of immigration. We find that the diversity scores for both Fragmentation and Representation are lower for immigration than for DST. Further, we find that while pro-immigrant views receive consistent pushback on the platform, anti-immigrant views largely operate within echo chambers. We observe less severe yet similar patterns for DST. Taken together, Representation and Fragmentation paint a meaningful and important new picture of viewpoint diversity.

This volume contains revised versions of the papers selected for the third volume of the Online Handbook of Argumentation for AI (OHAAI). Previously, formal theories of argument and argument interaction have been proposed and studied, and this has led to the more recent study of computational models of argument. Argumentation, as a field within artificial intelligence (AI), is highly relevant for researchers interested in symbolic representations of knowledge and defeasible reasoning. The purpose of this handbook is to provide an open access and curated anthology for the argumentation research community. OHAAI is designed to serve as a research hub to keep track of the latest and upcoming PhD-driven research on the theory and application of argumentation in all areas related to AI.

Evaluating and comparing text-to-image models is a challenging problem. Significant advances in the field have recently been made, piquing interest of various industrial sectors. As a consequence, a gold standard in the field should cover a variety of tasks and application contexts. In this paper a novel evaluation approach is experimented, on the basis of: (i) a curated data set, made by high-quality royalty-free image-text pairs, divided into ten categories; (ii) a quantitative metric, the CLIP-score, (iii) a human evaluation task to distinguish, for a given text, the real and the generated images. The proposed method has been applied to the most recent models, i.e., DALLE2, Latent Diffusion, Stable Diffusion, GLIDE and Craiyon. Early experimental results show that the accuracy of the human judgement is fully coherent with the CLIP-score. The dataset has been made available to the public.

Real-time monocular 3D reconstruction is a challenging problem that remains unsolved. Although recent end-to-end methods have demonstrated promising results, tiny structures and geometric boundaries are hardly captured due to their insufficient supervision neglecting spatial details and oversimplified feature fusion ignoring temporal cues. To address the problems, we propose an end-to-end 3D reconstruction network SST, which utilizes Sparse estimated points from visual SLAM system as additional Spatial guidance and fuses Temporal features via a novel cross-modal attention mechanism, achieving more detailed reconstruction results. We propose a Local Spatial-Temporal Fusion module to exploit more informative spatial-temporal cues from multi-view color information and sparse priors, as well a Global Spatial-Temporal Fusion module to refine the local TSDF volumes with the world-frame model from coarse to fine. Extensive experiments on ScanNet and 7-Scenes demonstrate that SST outperforms all state-of-the-art competitors, whilst keeping a high inference speed at 59 FPS, enabling real-world applications with real-time requirements.