对象目标导航要求机器人在以前看不见的环境中找到并导航到目标对象类的实例。我们的框架会随着时间的推移逐步构建环境的语义图，然后根据语义映射重复选择一个长期目标（“ where to Go”）以找到目标对象实例。长期目标选择被称为基于视觉的深度强化学习问题。具体而言，对编码器网络进行了训练，可以从语义图中提取高级功能并选择长期目标。此外，我们还将数据增强和Q功能正则化合并，以使长期目标选择更有效。我们在AI栖息地3D模拟环境中使用照片现实的Gibson基准数据集进行了实验结果，以证明与最先进的数据驱动基线相比，标准措施的性能改善。

对象目标导航的最新方法依赖于增强学习，通常需要大量的计算资源和学习时间。我们提出了使用无互动学习（PONI）的对象导航的潜在功能，这是一种模块化方法，可以散布“在哪里看？”的技能？对于对象和“如何导航到（x，y）？”。我们的主要见解是“在哪里看？”可以纯粹将其视为感知问题，而没有环境相互作用就可以学习。为了解决这个问题，我们提出了一个网络，该网络可以预测两个在语义图上的互补电位功能，并使用它们来决定在哪里寻找看不见的对象。我们使用在自上而下的语义图的被动数据集上使用受监督的学习来训练潜在的功能网络，并将其集成到模块化框架中以执行对象目标导航。 Gibson和MatterPort3D的实验表明，我们的方法可实现对象目标导航的最新方法，同时减少培训计算成本高达1,600倍。可以使用代码和预训练的模型：https：//vision.cs.utexas.edu/projects/poni/

Efficient ObjectGoal navigation (ObjectNav) in novel environments requires an understanding of the spatial and semantic regularities in environment layouts. In this work, we present a straightforward method for learning these regularities by predicting the locations of unobserved objects from incomplete semantic maps. Our method differs from previous prediction-based navigation methods, such as frontier potential prediction or egocentric map completion, by directly predicting unseen targets while leveraging the global context from all previously explored areas. Our prediction model is lightweight and can be trained in a supervised manner using a relatively small amount of passively collected data. Once trained, the model can be incorporated into a modular pipeline for ObjectNav without the need for any reinforcement learning. We validate the effectiveness of our method on the HM3D and MP3D ObjectNav datasets. We find that it achieves the state-of-the-art on both datasets, despite not using any additional data for training.

在这项工作中，我们提出了一种用于图像目标导航的内存调格方法。早期的尝试，包括基于RL的基于RL的方法和基于SLAM的方法的概括性能差，或者在姿势/深度传感器上稳定稳定。我们的方法基于一个基于注意力的端到端模型，该模型利用情节记忆来学习导航。首先，我们以自我监督的方式训练一个国家安置的网络，然后将其嵌入以前访问的状态中的代理商的记忆中。我们的导航政策通过注意机制利用了此信息。我们通过广泛的评估来验证我们的方法，并表明我们的模型在具有挑战性的吉布森数据集上建立了新的最新技术。此外，与相关工作形成鲜明对比的是，我们仅凭RGB输入就实现了这种令人印象深刻的性能，而无需访问其他信息，例如位置或深度。

这项工作研究了图像目标导航问题，需要通过真正拥挤的环境引导具有嘈杂传感器和控制的机器人。最近的富有成效的方法依赖于深度加强学习，并学习模拟环境中的导航政策，这些环境比真实环境更简单。直接将这些训练有素的策略转移到真正的环境可能非常具有挑战性甚至危险。我们用由四个解耦模块组成的分层导航方法来解决这个问题。第一模块在机器人导航期间维护障碍物映射。第二个将定期预测实时地图上的长期目标。第三个计划碰撞命令集以导航到长期目标，而最终模块将机器人正确靠近目标图像。四个模块是单独开发的，以适应真实拥挤的情景中的图像目标导航。此外，分层分解对导航目标规划，碰撞避免和导航结束预测的学习进行了解耦，这在导航训练期间减少了搜索空间，并有助于改善以前看不见的真实场景的概括。我们通过移动机器人评估模拟器和现实世界中的方法。结果表明，我们的方法优于多种导航基线，可以在这些方案中成功实现导航任务。

We propose a simple data augmentation technique that can be applied to standard model-free reinforcement learning algorithms, enabling robust learning directly from pixels without the need for auxiliary losses or pre-training. The approach leverages input perturbations commonly used in computer vision tasks to transform input examples, as well as regularizing the value function and policy. Existing model-free approaches, such as Soft Actor-Critic (SAC) [22], are not able to train deep networks effectively from image pixels. However, the addition of our augmentation method dramatically improves SAC's performance, enabling it to reach state-of-the-art performance on the DeepMind control suite, surpassing model-based [23,38,24] methods and recently proposed contrastive learning [50]. Our approach, which we dub DrQ: Data-regularized Q, can be combined with any model-free reinforcement learning algorithm. We further demonstrate this by applying it to DQN [43] and significantly improve its data-efficiency on the Atari 100k [31] benchmark. An implementation can be found at https://sites. google.com/view/data-regularized-q.

Semantic navigation is necessary to deploy mobile robots in uncontrolled environments like our homes, schools, and hospitals. Many learning-based approaches have been proposed in response to the lack of semantic understanding of the classical pipeline for spatial navigation, which builds a geometric map using depth sensors and plans to reach point goals. Broadly, end-to-end learning approaches reactively map sensor inputs to actions with deep neural networks, while modular learning approaches enrich the classical pipeline with learning-based semantic sensing and exploration. But learned visual navigation policies have predominantly been evaluated in simulation. How well do different classes of methods work on a robot? We present a large-scale empirical study of semantic visual navigation methods comparing representative methods from classical, modular, and end-to-end learning approaches across six homes with no prior experience, maps, or instrumentation. We find that modular learning works well in the real world, attaining a 90% success rate. In contrast, end-to-end learning does not, dropping from 77% simulation to 23% real-world success rate due to a large image domain gap between simulation and reality. For practitioners, we show that modular learning is a reliable approach to navigate to objects: modularity and abstraction in policy design enable Sim-to-Real transfer. For researchers, we identify two key issues that prevent today's simulators from being reliable evaluation benchmarks - (A) a large Sim-to-Real gap in images and (B) a disconnect between simulation and real-world error modes - and propose concrete steps forward.

Two less addressed issues of deep reinforcement learning are (1) lack of generalization capability to new target goals, and (2) data inefficiency i.e., the model requires several (and often costly) episodes of trial and error to converge, which makes it impractical to be applied to real-world scenarios. In this paper, we address these two issues and apply our model to the task of target-driven visual navigation. To address the first issue, we propose an actor-critic model whose policy is a function of the goal as well as the current state, which allows to better generalize. To address the second issue, we propose AI2-THOR framework, which provides an environment with highquality 3D scenes and physics engine. Our framework enables agents to take actions and interact with objects. Hence, we can collect a huge number of training samples efficiently.We show that our proposed method (1) converges faster than the state-of-the-art deep reinforcement learning methods, (2) generalizes across targets and across scenes, (3) generalizes to a real robot scenario with a small amount of fine-tuning (although the model is trained in simulation), ( 4) is end-to-end trainable and does not need feature engineering, feature matching between frames or 3D reconstruction of the environment.The supplementary video can be accessed at the following link: https://youtu.be/SmBxMDiOrvs.

In recent years several learning approaches to point goal navigation in previously unseen environments have been proposed. They vary in the representations of the environments, problem decomposition, and experimental evaluation. In this work, we compare the state-of-the-art Deep Reinforcement Learning based approaches with Partially Observable Markov Decision Process (POMDP) formulation of the point goal navigation problem. We adapt the (POMDP) sub-goal framework proposed by [1] and modify the component that estimates frontier properties by using partial semantic maps of indoor scenes built from images' semantic segmentation. In addition to the well-known completeness of the model-based approach, we demonstrate that it is robust and efficient in that it leverages informative, learned properties of the frontiers compared to an optimistic frontier-based planner. We also demonstrate its data efficiency compared to the end-to-end deep reinforcement learning approaches. We compare our results against an optimistic planner, ANS and DD-PPO on Matterport3D dataset using the Habitat Simulator. We show comparable, though slightly worse performance than the SOTA DD-PPO approach, yet with far fewer data.

本文描述了对象目标导航任务的框架，该任务要求机器人从随机的启动位置查找并移至目标对象类的最接近实例。该框架使用机器人轨迹的历史记录来学习空间关系图（SRG）和图形卷积网络（GCN）基于基于不同语义标记区域的可能性以及这些区域不同对象类别的发生的可能性。为了在评估过程中定位目标对象实例，机器人使用贝叶斯推理和SRG估计可见区域，并使用学习的GCN嵌入来对可见区域进行排名，并选择接下来的区域。

Object goal navigation (ObjectNav) in unseen environments is a fundamental task for Embodied AI. Agents in existing works learn ObjectNav policies based on 2D maps, scene graphs, or image sequences. Considering this task happens in 3D space, a 3D-aware agent can advance its ObjectNav capability via learning from fine-grained spatial information. However, leveraging 3D scene representation can be prohibitively unpractical for policy learning in this floor-level task, due to low sample efficiency and expensive computational cost. In this work, we propose a framework for the challenging 3D-aware ObjectNav based on two straightforward sub-policies. The two sub-polices, namely corner-guided exploration policy and category-aware identification policy, simultaneously perform by utilizing online fused 3D points as observation. Through extensive experiments, we show that this framework can dramatically improve the performance in ObjectNav through learning from 3D scene representation. Our framework achieves the best performance among all modular-based methods on the Matterport3D and Gibson datasets, while requiring (up to 30x) less computational cost for training.

对象目标导航（ObjectNAV）任务是在没有预先构建的地图的情况下将代理导航到看不见的环境中的对象类别。在本文中，我们通过使用语义相关对象作为线索来预测目标的距离来解决此任务。根据与目标对象的估计距离，我们的方法直接选择最佳的中期目标，这些目标更可能具有较短的目标途径。具体而言，基于学习的知识，我们的模型将鸟眼视图语义图作为输入，并估算从边界图单元到目标对象的路径长度。借助估计的距离图，代理可以同时探索环境并基于简单的人类设计策略导航到目标对象。在视觉上逼真的模拟环境中，经验结果表明，该提出的方法的表现优于成功率和效率的广泛基准。 Realobot实验还表明，我们的方法很好地推广到了现实世界。视频https://www.youtube.com/watch?v=r79pwvgfks4

本文介绍了一些最先进的加强学习算法的基准研究，用于解决两个模拟基于视觉的机器人问题。本研究中考虑的算法包括软演员 - 评论家（SAC），近端政策优化（PPO），内插政策梯度（IPG），以及与后敏感体验重播（她）的变体。将这些算法的性能与Pybullet的两个仿真环境进行比较，称为KukadiverseObjectenV和raceCarzedgymenv。这些环境中的状态观察以RGB图像的形式提供，并且动作空间是连续的，使得它们难以解决。建议许多策略提供在基本上单目标环境的这些问题上实施算法所需的中级后敏感目标。另外，提出了许多特征提取架构在学习过程中纳入空间和时间关注。通过严格的模拟实验，建立了这些组分实现的改进。据我们所知，这种基准测试的基础基础是基于视觉的机器人问题的基准研究，使其成为该领域的新贡献。

强化学习（RL）通过原始像素成像和连续的控制任务在视频游戏中表现出了令人印象深刻的表现。但是，RL的性能较差，例如原始像素图像，例如原始像素图像。人们普遍认为，基于物理状态的RL策略（例如激光传感器测量值）比像素学习相比会产生更有效的样品结果。这项工作提出了一种新方法，该方法从深度地图估算中提取信息，以教授RL代理以执行无人机导航（UAV）的无地图导航。我们提出了深度模仿的对比度无监督的优先表示（DEPTH-CUPRL），该表示具有优先重播记忆的估算图像的深度。我们使用RL和对比度学习的组合，根据图像的RL问题引发。从无人驾驶汽车（UAV）对结果的分析中，可以得出结论，我们的深度cuprl方法在无MAP导航能力中对决策和优于最先进的像素的方法有效。

为了基于深度加强学习（RL）来增强目标驱动的视觉导航的交叉目标和跨场景，我们将信息理论正则化术语引入RL目标。正则化最大化导航动作与代理的视觉观察变换之间的互信息，从而促进更明智的导航决策。这样，代理通过学习变分生成模型来模拟动作观察动态。基于该模型，代理生成（想象）从其当前观察和导航目标的下一次观察。这样，代理学会了解导航操作与其观察变化之间的因果关系，这允许代理通过比较当前和想象的下一个观察来预测导航的下一个动作。 AI2-Thor框架上的交叉目标和跨场景评估表明，我们的方法在某些最先进的模型上获得了平均成功率的10美元。我们进一步评估了我们的模型在两个现实世界中：来自离散的活动视觉数据集（AVD）和带有TurtleBot的连续现实世界环境中的看不见的室内场景导航。我们证明我们的导航模型能够成功实现导航任务这些情景。视频和型号可以在补充材料中找到。

深度强化学习在基于激光的碰撞避免有效的情况下取得了巨大的成功，因为激光器可以感觉到准确的深度信息而无需太多冗余数据，这可以在算法从模拟环境迁移到现实世界时保持算法的稳健性。但是，高成本激光设备不仅很难为大型机器人部署，而且还表现出对复杂障碍的鲁棒性，包括不规则的障碍，例如桌子，桌子，椅子和架子，以及复杂的地面和特殊材料。在本文中，我们提出了一个新型的基于单眼相机的复杂障碍避免框架。特别是，我们创新地将捕获的RGB图像转换为伪激光测量，以进行有效的深度强化学习。与在一定高度捕获的传统激光测量相比，仅包含距离附近障碍的一维距离信息，我们提议的伪激光测量融合了捕获的RGB图像的深度和语义信息，这使我们的方法有效地有效障碍。我们还设计了一个功能提取引导模块，以加重输入伪激光测量，并且代理对当前状态具有更合理的关注，这有利于提高障碍避免政策的准确性和效率。

最近的视听导航工作是无噪音音频环境中的单一静态声音，并努力推广到闻名声音。我们介绍了一种新型动态视听导航基准测试，其中一个体现的AI代理必须在存在分散的人和嘈杂的声音存在下在未映射的环境中捕获移动声源。我们提出了一种依赖于多模态架构的端到端增强学习方法，该方法依赖于融合来自双耳音频信号和空间占用映射的空间视听信息，以编码为我们的新的稳健导航策略进行编码所需的功能复杂的任务设置。我们展示了我们的方法优于当前的最先进状态，以更好地推广到闻名声音以及对嘈杂的3D扫描现实世界数据集副本和TASTPORT3D上的嘈杂情景更好地对嘈杂的情景进行了更好的稳健性，以实现静态和动态的视听导航基准。我们的小型基准将在http://dav-nav.cs.uni-freiburg.de提供。

无监督的表示学习的最新进展显着提高了模拟环境中培训强化学习政策的样本效率。但是，尚未看到针对实体强化学习的类似收益。在这项工作中，我们专注于从像素中启用数据有效的实体机器人学习。我们提出了有效的机器人学习（编码器）的对比前训练和数据增强，该方法利用数据增强和无监督的学习来从稀疏奖励中实现对实体ARM策略的样本效率培训。虽然对比预训练，数据增强，演示和强化学习不足以进行有效学习，但我们的主要贡献表明，这些不同技术的组合导致了一种简单而数据效率的方法。我们表明，只有10个示范，一个机器人手臂可以从像素中学习稀疏的奖励操纵策略，例如到达，拾取，移动，拉动大物体，翻转开关并在短短30分钟内打开抽屉现实世界训练时间。我们在项目网站上包括视频和代码：https：//sites.google.com/view/felfficited-robotic-manipulation/home

We present a retrospective on the state of Embodied AI research. Our analysis focuses on 13 challenges presented at the Embodied AI Workshop at CVPR. These challenges are grouped into three themes: (1) visual navigation, (2) rearrangement, and (3) embodied vision-and-language. We discuss the dominant datasets within each theme, evaluation metrics for the challenges, and the performance of state-of-the-art models. We highlight commonalities between top approaches to the challenges and identify potential future directions for Embodied AI research.

可视探索是一种任务，可以尽快访问环境的所有可通航区域。现有方法采用深度加强学习（RL）作为任务的标准工具。然而，它们往往容易受到训练和测试数据之间的统计变化，导致从训练数据的分发（OOD）的新环境中的概括不良。在本文中，我们试图通过利用可用于任务的归纳偏差来改善泛化能力。采用主动神经血液（ANS），了解探索政策的优势演员 - 评论家（A2C）方法作为基础框架，首先指出演员和评论家代表的映射应该满足特定的对称性。然后，我们为演员和批评者提出了一个网络设计，并批评了本身达到这些对称性。具体而言，我们使用$ g $ -convolution而不是标准卷积，并在批评网络的最后一节中插入我们新设计的半全局极性池（SGPP）层。实验结果表明，当在Gibson数据集上培训并在MP3D数据集上进行测试时，我们的方法增加了8.1米^ 2 $ 8.1 m ^ 2 $。