可以与其他代理人互动以完成给定任务的自主代理的发展是人工智能和机器学习研究的核心领域。为了实现这一目标，自主代理研究小组开发了用于自主系统控制的新型机器学习算法，特别关注深度强化学习和多代理强化学习。研究问题包括可扩展的协调代理政策和代理间沟通；从有限观察的情况下对其他代理的行为，目标和组成的推理；以及基于内在动机，课程学习，因果推断和代表性学习的样品学习。本文概述了该小组正在进行的研究组合，并讨论了未来方向的开放问题。

临时团队合作（AHT）是创建一个必须与以前看不见的队友合作而没有事先协调的问题。许多现有的AHT方法可以归类为基于类型的方法，这些方法需要一组预定义的队友进行培训。为训练设计队友类型是一个具有挑战性的问题，它决定了在训练期间与队友类型打交道时的代理商的概括性能。在这项工作中，我们提出了一种基于最大化最佳响应多样性指标的不同队友类型的方法。我们表明，我们提出的方法会产生队友类型，这些类型需要在协作期间从学习者那里获得更广泛的最佳反应，这可能会提高学习者在AHT中的稳健性与替代方法相比。

我们提出了小说的少量团队合作（FST）问题，在该问题中，在团队中训练有素的熟练代理人完成一项任务与来自不同任务的熟练代理相结合，并且必须共同学习适应一个看不见但相关的任务。我们讨论如何将FST问题视为解决两个单独的问题：一种减少培训代理团队完成复杂任务所需的经验；与陌生队友合作完成了一项新任务。解决FST的进展可能会导致多方面的强化学习和临时团队合作的进步。

我们提出了Midgard，这是一个用于室外非结构化环境中自动机器人导航的开源模拟平台。 Midgard旨在实现在影照相3D环境中对自主代理（例如，无人接地车）进行培训，并通过培训场景中的可变性来支持基于学习的代理的概括技巧。 Midgard的主要功能包括可配置，可扩展和难度驱动的程序景观生成管道，并具有基于虚幻引擎的快速和影像现实主义场景。此外，Midgard还对OpenAi Gym进行了内置支持，OpenAi Gym是一个用于功能扩展的编程接口（例如，集成新型的传感器，自定义曝光内部模拟变量）和各种模拟代理传感器（例如RGB，DEPTH和实例/实例/语义细分）。我们评估了Midgard的功能，作为使用一组最先进的强化学习算法的机器人导航的基准测试工具。结果表明，Midgard作为模拟和训练环境的适用性，以及我们程序生成方法在控制场景难度方面的有效性，这直接反映了准确度量指标。 Midgard构建，源代码和文档可在https://midgardsim.org/上找到。

临时团队合作是设计可以与新队友合作而无需事先协调的研究问题的研究问题。这项调查做出了两个贡献：首先，它提供了对临时团队工作问题不同方面的结构化描述。其次，它讨论了迄今为止该领域取得的进展，并确定了临时团队工作中需要解决的直接和长期开放问题。

Spacecraft pose estimation is a key task to enable space missions in which two spacecrafts must navigate around each other. Current state-of-the-art algorithms for pose estimation employ data-driven techniques. However, there is an absence of real training data for spacecraft imaged in space conditions due to the costs and difficulties associated with the space environment. This has motivated the introduction of 3D data simulators, solving the issue of data availability but introducing a large gap between the training (source) and test (target) domains. We explore a method that incorporates 3D structure into the spacecraft pose estimation pipeline to provide robustness to intensity domain shift and we present an algorithm for unsupervised domain adaptation with robust pseudo-labelling. Our solution has ranked second in the two categories of the 2021 Pose Estimation Challenge organised by the European Space Agency and the Stanford University, achieving the lowest average error over the two categories.

Petrov-Galerkin formulations with optimal test functions allow for the stabilization of finite element simulations. In particular, given a discrete trial space, the optimal test space induces a numerical scheme delivering the best approximation in terms of a problem-dependent energy norm. This ideal approach has two shortcomings: first, we need to explicitly know the set of optimal test functions; and second, the optimal test functions may have large supports inducing expensive dense linear systems. Nevertheless, parametric families of PDEs are an example where it is worth investing some (offline) computational effort to obtain stabilized linear systems that can be solved efficiently, for a given set of parameters, in an online stage. Therefore, as a remedy for the first shortcoming, we explicitly compute (offline) a function mapping any PDE-parameter, to the matrix of coefficients of optimal test functions (in a basis expansion) associated with that PDE-parameter. Next, as a remedy for the second shortcoming, we use the low-rank approximation to hierarchically compress the (non-square) matrix of coefficients of optimal test functions. In order to accelerate this process, we train a neural network to learn a critical bottleneck of the compression algorithm (for a given set of PDE-parameters). When solving online the resulting (compressed) Petrov-Galerkin formulation, we employ a GMRES iterative solver with inexpensive matrix-vector multiplications thanks to the low-rank features of the compressed matrix. We perform experiments showing that the full online procedure as fast as the original (unstable) Galerkin approach. In other words, we get the stabilization with hierarchical matrices and neural networks practically for free. We illustrate our findings by means of 2D Eriksson-Johnson and Hemholtz model problems.

To alleviate the problem of structured databases' limited coverage, recent task-oriented dialogue systems incorporate external unstructured knowledge to guide the generation of system responses. However, these usually use word or sentence level similarities to detect the relevant knowledge context, which only partially capture the topical level relevance. In this paper, we examine how to better integrate topical information in knowledge grounded task-oriented dialogue and propose ``Topic-Aware Response Generation'' (TARG), an end-to-end response generation model. TARG incorporates multiple topic-aware attention mechanisms to derive the importance weighting scheme over dialogue utterances and external knowledge sources towards a better understanding of the dialogue history. Experimental results indicate that TARG achieves state-of-the-art performance in knowledge selection and response generation, outperforming previous state-of-the-art by 3.2, 3.6, and 4.2 points in EM, F1 and BLEU-4 respectively on Doc2Dial, and performing comparably with previous work on DSTC9; both being knowledge-grounded task-oriented dialogue datasets.

Video provides us with the spatio-temporal consistency needed for visual learning. Recent approaches have utilized this signal to learn correspondence estimation from close-by frame pairs. However, by only relying on close-by frame pairs, those approaches miss out on the richer long-range consistency between distant overlapping frames. To address this, we propose a self-supervised approach for correspondence estimation that learns from multiview consistency in short RGB-D video sequences. Our approach combines pairwise correspondence estimation and registration with a novel SE(3) transformation synchronization algorithm. Our key insight is that self-supervised multiview registration allows us to obtain correspondences over longer time frames; increasing both the diversity and difficulty of sampled pairs. We evaluate our approach on indoor scenes for correspondence estimation and RGB-D pointcloud registration and find that we perform on-par with supervised approaches.

In this work we propose a novel token-based training strategy that improves Transformer-Transducer (T-T) based speaker change detection (SCD) performance. The conventional T-T based SCD model loss optimizes all output tokens equally. Due to the sparsity of the speaker changes in the training data, the conventional T-T based SCD model loss leads to sub-optimal detection accuracy. To mitigate this issue, we use a customized edit-distance algorithm to estimate the token-level SCD false accept (FA) and false reject (FR) rates during training and optimize model parameters to minimize a weighted combination of the FA and FR, focusing the model on accurately predicting speaker changes. We also propose a set of evaluation metrics that align better with commercial use cases. Experiments on a group of challenging real-world datasets show that the proposed training method can significantly improve the overall performance of the SCD model with the same number of parameters.