In this paper, we examine the problem of visibility-aware robot navigation among movable obstacles (VANAMO). A variant of the well-known NAMO robotic planning problem, VANAMO puts additional visibility constraints on robot motion and object movability. This new problem formulation lifts the restrictive assumption that the map is fully visible and the object positions are fully known. We provide a formal definition of the VANAMO problem and propose the Look and Manipulate Backchaining (LaMB) algorithm for solving such problems. LaMB has a simple vision-based API that makes it more easily transferable to real-world robot applications and scales to the large 3D environments. To evaluate LaMB, we construct a set of tasks that illustrate the complex interplay between visibility and object movability that can arise in mobile base manipulation problems in unknown environments. We show that LaMB outperforms NAMO and visibility-aware motion planning approaches as well as simple combinations of them on complex manipulation problems with partial observability.

在环境抽象中进行高级搜索来指导低水平决策，这是一种有效的方法，是解决连续状态和行动空间中的长途任务的有效方法。最近的工作表明，可以以符号操作员和神经采样器的形式学习使这种二聚体计划的动作抽象，并且鉴于实现已知目标的符号谓词和演示。在这项工作中，我们表明，在动作往往会导致大量谓词发生变化的环境中，现有的方法不足。为了解决这个问题，我们建议学习具有忽略效果的操作员。激发我们方法的关键思想是，对谓词的每一个观察到的变化进行建模是不必要的。唯一需要建模的更改是高级搜索以实现指定目标所需的更改。在实验上，我们表明我们的方法能够学习具有忽略六个混合机器人域效果的操作员，这些企业能够解决一个代理，以解决具有不同初始状态，目标和对象数量的新任务变化，比几个基线要高得多。

在具有连续以对象的状态，连续的动作，长距离和稀疏反馈的机器人环境中，决策是具有挑战性的。诸如任务和运动计划（TAMP）之类的层次结构方法通过将决策分解为两个或更多级别的抽象来解决这些挑战。在给出演示和符号谓词的环境中，先前的工作已经显示了如何通过手动设计的参数化策略来学习符号操作员和神经采样器。我们的主要贡献是一种与操作员和采样器结合使用的参数化策略的方法。这些组件被包装到模块化神经符号技能中，并与搜索 - 然后样本tamp一起测序以解决新任务。在四个机器人域的实验中，我们表明我们的方法 - 具有神经符号技能的双重计划 - 可以解决具有不同初始状态，目标和对象不同的各种任务，表现优于六个基线和消融。视频：https：//youtu.be/pbfzp8rpugg代码：https：//tinyurl.com/skill-learning

本文介绍了在公开不确定域中描述和解释机器人目标的问题的集成解决方案。鉴于所需情况的正式规范，其中仅通过其性质描述了对象，通用规划和推理工具用于导出机器人的适当行动。这些目标是通过在线组合的分层规划，状态估计和执行，其在具有实质上闭塞和传感误差的真实机器人域中运行的鲁棒性。

在机器人域中，学习和计划因连续的状态空间，连续的动作空间和较长的任务范围而变得复杂。在这项工作中，我们通过神经符号关系过渡模型（NSRTS）解决了这些挑战，这是一种具有数据效率学习的新型模型，与强大的机器人计划方法兼容，并且可以推广到对象上。NSRT具有符号和神经成分，实现了双重计划方案，其中外循环中的符号AI规划指导内部循环中的神经模型的连续计划。四个机器人计划域中的实验表明，仅在数十或数百个培训情节之后就可以学习NSRT，然后用于快速规划的新任务，这些任务需要高达60个动作，并且涉及比培训期间看到的更多物体。视频：https：//tinyurl.com/chitnis-nsrts

This paper provides theoretical insights into why and how deep learning can generalize well, despite its large capacity, complexity, possible algorithmic instability, nonrobustness, and sharp minima, responding to an open question in the literature. We also discuss approaches to provide non-vacuous generalization guarantees for deep learning. Based on theoretical observations, we propose new open problems and discuss the limitations of our results.

Representing and reasoning about uncertainty is crucial for autonomous agents acting in partially observable environments with noisy sensors. Partially observable Markov decision processes (POMDPs) serve as a general framework for representing problems in which uncertainty is an important factor. Online sample-based POMDP methods have emerged as efficient approaches to solving large POMDPs and have been shown to extend to continuous domains. However, these solutions struggle to find long-horizon plans in problems with significant uncertainty. Exploration heuristics can help guide planning, but many real-world settings contain significant task-irrelevant uncertainty that might distract from the task objective. In this paper, we propose STRUG, an online POMDP solver capable of handling domains that require long-horizon planning with significant task-relevant and task-irrelevant uncertainty. We demonstrate our solution on several temporally extended versions of toy POMDP problems as well as robotic manipulation of articulated objects using a neural perception frontend to construct a distribution of possible models. Our results show that STRUG outperforms the current sample-based online POMDP solvers on several tasks.

在家庭中运行的机器人可以观察到多个物体在几天或几周内移动时。这些物体可以被居民移动，但不是完全随机的。稍后可以要求机器人检索对象，并需要一个基于对象的内存才能知道如何找到它们。语义大满贯中现有的工作并不试图捕获对象运动的动态。在本文中，我们将用于数据缔合过滤的经典技术与现代化的神经网络相结合，以构建基于对象的内存系统，这些系统在高维观察和假设上运行。我们对标记的观察轨迹进行端到端学习，以学习过渡和观察模型。我们证明了系统在模拟环境和真实图像中动态变化对象的记忆方面的有效性，并证明了对经典结构化方法以及非结构化神经方法的改进。在项目网站上获得的其他信息：https：//yilundu.github.io/obm/。

Recent object detection models for infrared (IR) imagery are based upon deep neural networks (DNNs) and require large amounts of labeled training imagery. However, publicly-available datasets that can be used for such training are limited in their size and diversity. To address this problem, we explore cross-modal style transfer (CMST) to leverage large and diverse color imagery datasets so that they can be used to train DNN-based IR image based object detectors. We evaluate six contemporary stylization methods on four publicly-available IR datasets - the first comparison of its kind - and find that CMST is highly effective for DNN-based detectors. Surprisingly, we find that existing data-driven methods are outperformed by a simple grayscale stylization (an average of the color channels). Our analysis reveals that existing data-driven methods are either too simplistic or introduce significant artifacts into the imagery. To overcome these limitations, we propose meta-learning style transfer (MLST), which learns a stylization by composing and tuning well-behaved analytic functions. We find that MLST leads to more complex stylizations without introducing significant image artifacts and achieves the best overall detector performance on our benchmark datasets.

Estimating the state of charge (SOC) of compound energy storage devices in the hybrid energy storage system (HESS) of electric vehicles (EVs) is vital in improving the performance of the EV. The complex and variable charging and discharging current of EVs makes an accurate SOC estimation a challenge. This paper proposes a novel deep learning-based SOC estimation method for lithium-ion battery-supercapacitor HESS EV based on the nonlinear autoregressive with exogenous inputs neural network (NARXNN). The NARXNN is utilized to capture and overcome the complex nonlinear behaviors of lithium-ion batteries and supercapacitors in EVs. The results show that the proposed method improved the SOC estimation accuracy by 91.5% on average with error values below 0.1% and reduced consumption time by 11.4%. Hence validating both the effectiveness and robustness of the proposed method.