人类具有非凡的能力来传达和阅读对象的属性，只需看到它们被别人带走即可。人类可用的这种沟通技巧和解释水平对于协作机器人可以自然和有效的互动对于协作机器人至关重要。例如，假设机器人正在移交一个脆弱的对象。在这种情况下，应通过直接和隐性的信息，即通过直接调节机器人的行动来告知其脆弱性的人。这项工作调查了两个具有不同实施方案的机器人（一个ICUB类人体机器人和Baxter机器人）进行交流意图执行的对象操作的感知。我们设计了机器人的动作，以传达对象运输过程中的谨慎性。我们发现，人类观察者不仅可以正确地感知此功能，而且可以在随后的人类物体操纵中引起运动适应的一种形式。此外，我们可以深入了解哪些运动功能可能会或多或少地谨慎地操纵物体。

密切的人类机器人互动（HRI），尤其是在工业场景中，已经对结合人类和机器人技能的优势进行了广泛的研究。对于有效的HRI，应质疑当前可用的人机通信媒体或工具的有效性，并应探讨新的交流方式。本文提出了一个模块化体系结构，允许人类操作员通过不同的方式与机器人互动。特别是，我们使用智能手表和平板电脑分别实施了架构来分别处理手势和触摸屏输入。最后，我们在这两种方式之间进行了比较用户体验研究。

从事协作活动的人类自然能够通过多模式交流将其意图传达给队友，这是由明确和隐性的提示组成的。同样，可以通过使机器人能够通过多个沟通渠道将其意图传达给人类队友，从而实现更自然的人类机器人协作形式。在本文中，我们假设如果协作机器人能够以直观的方式将他们的动作预期到人类队友，则可以进行更好的沟通。为了支持这种说法，我们提出了一个机器人系统的架构，通过该架构，机器人可以通过该架构将计划的动作传达给人类队友，以利用由现代头部安装的显示器提供支持的混合现实接口。具体而言，在人类队友的角度叠加到真正的机器人的机器人全息图显示了机器人的未来运动，使人类可以事先理解它们，并可能以适当的方式对它们做出反应。我们进行了初步的用户研究，以评估复杂的协作任务中提出的预期可视化的有效性。实验结果表明，通过采用这种预期的沟通模式可以改善和自然的协作。

尽管Cobots具有高潜力，但在制造和后勤过程中带来了几个好处，但它们在不断变化环境中的快速（重新）部署仍然有限。为了实现快速适应新产品的需求，并提高人工人员对分配任务的适应性，我们提出了一种新的方法，可以优化装配策略，并在人机合作任务中分配工人之间的努力。合作模型利用和/或图表，我们适于解决角色分配问题。分配算法考虑在线计算的定量测量，以描述人工人工符的符合人体工程学状态和任务属性。我们进行了初步实验，以证明拟议的方法成功控制任务分配过程，以确保人工人工的安全和符合人体工程学条件。

当操纵对象时，人类将它们的动作精细调整到他们正在处理的特征。因此，细心观察者可以预见被操纵物体的隐藏性质，例如其重量，温度，甚至它是否需要特别注意操纵。这项研究是朝着赋予人类机器人的一步，这是一个最后的能力。具体而言，我们研究机器人如何从单独推断出在线推断，无论是人类伴侣在移动物体时都是小心的。我们表明，即使使用低分辨率摄像头，人形机器人也可以高精度地执行此推理（高达81.3％）。只有短暂的运动没有障碍，仔细识别不足。迅速识别出现谨慎观察合作伙伴的行动将使机器人能够适应对象的行为，以显示与人工合作伙伴相同程度的照顾。

Computational units in artificial neural networks follow a simplified model of biological neurons. In the biological model, the output signal of a neuron runs down the axon, splits following the many branches at its end, and passes identically to all the downward neurons of the network. Each of the downward neurons will use their copy of this signal as one of many inputs dendrites, integrate them all and fire an output, if above some threshold. In the artificial neural network, this translates to the fact that the nonlinear filtering of the signal is performed in the upward neuron, meaning that in practice the same activation is shared between all the downward neurons that use that signal as their input. Dendrites thus play a passive role. We propose a slightly more complex model for the biological neuron, where dendrites play an active role: the activation in the output of the upward neuron becomes optional, and instead the signals going through each dendrite undergo independent nonlinear filterings, before the linear combination. We implement this new model into a ReLU computational unit and discuss its biological plausibility. We compare this new computational unit with the standard one and describe it from a geometrical point of view. We provide a Keras implementation of this unit into fully connected and convolutional layers and estimate their FLOPs and weights change. We then use these layers in ResNet architectures on CIFAR-10, CIFAR-100, Imagenette, and Imagewoof, obtaining performance improvements over standard ResNets up to 1.73%. Finally, we prove a universal representation theorem for continuous functions on compact sets and show that this new unit has more representational power than its standard counterpart.

Fruit is a key crop in worldwide agriculture feeding millions of people. The standard supply chain of fruit products involves quality checks to guarantee freshness, taste, and, most of all, safety. An important factor that determines fruit quality is its stage of ripening. This is usually manually classified by experts in the field, which makes it a labor-intensive and error-prone process. Thus, there is an arising need for automation in the process of fruit ripeness classification. Many automatic methods have been proposed that employ a variety of feature descriptors for the food item to be graded. Machine learning and deep learning techniques dominate the top-performing methods. Furthermore, deep learning can operate on raw data and thus relieve the users from having to compute complex engineered features, which are often crop-specific. In this survey, we review the latest methods proposed in the literature to automatize fruit ripeness classification, highlighting the most common feature descriptors they operate on.

Artificial neural networks can learn complex, salient data features to achieve a given task. On the opposite end of the spectrum, mathematically grounded methods such as topological data analysis allow users to design analysis pipelines fully aware of data constraints and symmetries. We introduce a class of persistence-based neural network layers. Persistence-based layers allow the users to easily inject knowledge about symmetries (equivariance) respected by the data, are equipped with learnable weights, and can be composed with state-of-the-art neural architectures.

In this work we introduce reinforcement learning techniques for solving lexicographic multi-objective problems. These are problems that involve multiple reward signals, and where the goal is to learn a policy that maximises the first reward signal, and subject to this constraint also maximises the second reward signal, and so on. We present a family of both action-value and policy gradient algorithms that can be used to solve such problems, and prove that they converge to policies that are lexicographically optimal. We evaluate the scalability and performance of these algorithms empirically, demonstrating their practical applicability. As a more specific application, we show how our algorithms can be used to impose safety constraints on the behaviour of an agent, and compare their performance in this context with that of other constrained reinforcement learning algorithms.

In contextual linear bandits, the reward function is assumed to be a linear combination of an unknown reward vector and a given embedding of context-arm pairs. In practice, the embedding is often learned at the same time as the reward vector, thus leading to an online representation learning problem. Existing approaches to representation learning in contextual bandits are either very generic (e.g., model-selection techniques or algorithms for learning with arbitrary function classes) or specialized to particular structures (e.g., nested features or representations with certain spectral properties). As a result, the understanding of the cost of representation learning in contextual linear bandit is still limited. In this paper, we take a systematic approach to the problem and provide a comprehensive study through an instance-dependent perspective. We show that representation learning is fundamentally more complex than linear bandits (i.e., learning with a given representation). In particular, learning with a given set of representations is never simpler than learning with the worst realizable representation in the set, while we show cases where it can be arbitrarily harder. We complement this result with an extensive discussion of how it relates to existing literature and we illustrate positive instances where representation learning is as complex as learning with a fixed representation and where sub-logarithmic regret is achievable.