In communication restricted environments, a multi-robot system can be
deployed to either: i) maintain constant communication but potentially
sacrifice operational efficiency due to proximity constraints or ii) allow
disconnections to increase environmental coverage efficiency, challenges on
how, when, and where to reconnect (rendezvous problem). In this work we tackle
the latter problem and notice that most state-of-the-art methods assume that
robots will be able to execute a predetermined plan; however system failures
and changes in environmental conditions can cause the robots to deviate from
the plan with cascading effects across the multi-robot system. This paper
proposes a coordinated epistemic prediction and planning framework to achieve
consensus without communicating for exploration and coverage, task discovery
and completion, and rendezvous applications. Dynamic epistemic logic is the
principal component implemented to allow robots to propagate belief states and
empathize with other agents. Propagation of belief states and subsequent
coverage of the environment is achieved via a frontier-based method within an
artificial physics-based framework. The proposed framework is validated with
both simulations and experiments with unmanned ground vehicles in various
cluttered environments.

本篇论文提出了一种基于协调的认知预测和规划框架来解决多机器人系统中的非通信性渲染问题，同时实现了探索、覆盖、任务发现和完成以及汇合应用等任务，该框架通过基于前沿的方法在人工物理学框架内实现置信状态的传播和环境覆盖，经过模拟和实验验证了其有效性。

通信受限环境下多机器人团队的认知预测与隐式协调计划

Epistemic Prediction and Planning with Implicit Coordination for Multi-Robot Teams in Communication Restricted Environments

We study how to leverage off-the-shelf visual and linguistic data to cope
with out-of-vocabulary answers in visual question answering task. Existing
large-scale visual datasets with annotations such as image class labels,
bounding boxes and region descriptions are good sources for learning rich and
diverse visual concepts. However, it is not straightforward how the visual
concepts can be captured and transferred to visual question answering models
due to missing link between question dependent answering models and visual data
without question. We tackle this problem in two steps: 1) learning a task
conditional visual classifier, which is capable of solving diverse
question-specific visual recognition tasks, based on unsupervised task
discovery and 2) transferring the task conditional visual classifier to visual
question answering models. Specifically, we employ linguistic knowledge sources
such as structured lexical database (e.g. WordNet) and visual descriptions for
unsupervised task discovery, and transfer a learned task conditional visual
classifier as an answering unit in a visual question answering model. We
empirically show that the proposed algorithm generalizes to out-of-vocabulary
answers successfully using the knowledge transferred from the visual dataset.

通过使用视觉和语言数据，利用无监督任务发现学习一个任务特定的视觉分类器，并将其转移到视觉问答模型中作为答案单元，以解决视觉问题中的词汇表外问题，并通过从视觉数据转移的知识成功推广以应对此问题。

通过无监督任务发现的迁移学习，用于视觉问答

Transfer Learning via Unsupervised Task Discovery for Visual Question  Answering

Reinforcement learning is a powerful technique to train an agent to perform a
task. However, an agent that is trained using reinforcement learning is only
capable of achieving the single task that is specified via its reward function.
Such an approach does not scale well to settings in which an agent needs to
perform a diverse set of tasks, such as navigating to varying positions in a
room or moving objects to varying locations. Instead, we propose a method that
allows an agent to automatically discover the range of tasks that it is capable
of performing. We use a generator network to propose tasks for the agent to try
to achieve, specified as goal states. The generator network is optimized using
adversarial training to produce tasks that are always at the appropriate level
of difficulty for the agent. Our method thus automatically produces a
curriculum of tasks for the agent to learn. We show that, by using this
framework, an agent can efficiently and automatically learn to perform a wide
set of tasks without requiring any prior knowledge of its environment. Our
method can also learn to achieve tasks with sparse rewards, which traditionally
pose significant challenges.

提出了一种基于 Adversarial training 的方法，用于 Reinforcement learning 中任务发现的问题，可以实现在不需要任何先验环境知识的情况下，对多元化任务的高效自动学习，并且能够解决传统上存在的稀疏奖励问题。