We consider the problem of embodied visual navigation given an image-goal (ImageNav) where an agent is initialized in an unfamiliar environment and tasked with navigating to a location 'described' by an image. Unlike related navigation tasks, ImageNav does not have a standardized task definition which makes comparison across methods difficult. Further, existing formulations have two problematic properties; (1) image-goals are sampled from random locations which can lead to ambiguity (e.g., looking at walls), and (2) image-goals match the camera specification and embodiment of the agent; this rigidity is limiting when considering user-driven downstream applications. We present the Instance-specific ImageNav task (InstanceImageNav) to address these limitations. Specifically, the goal image is 'focused' on some particular object instance in the scene and is taken with camera parameters independent of the agent. We instantiate InstanceImageNav in the Habitat Simulator using scenes from the Habitat-Matterport3D dataset (HM3D) and release a standardized benchmark to measure community progress.

A robot that can carry out a natural-language instruction has been a dream since before the Jetsons cartoon series imagined a life of leisure mediated by a fleet of attentive robot helpers. It is a dream that remains stubbornly distant. However, recent advances in vision and language methods have made incredible progress in closely related areas. This is significant because a robot interpreting a naturallanguage navigation instruction on the basis of what it sees is carrying out a vision and language process that is similar to Visual Question Answering. Both tasks can be interpreted as visually grounded sequence-to-sequence translation problems, and many of the same methods are applicable. To enable and encourage the application of vision and language methods to the problem of interpreting visuallygrounded navigation instructions, we present the Matter-port3D Simulator -a large-scale reinforcement learning environment based on real imagery [11]. Using this simulator, which can in future support a range of embodied vision and language tasks, we provide the first benchmark dataset for visually-grounded natural language navigation in real buildings -the Room-to-Room (R2R) dataset 1 .1 https://bringmeaspoon.org Instruction: Head upstairs and walk past the piano through an archway directly in front. Turn right when the hallway ends at pictures and table. Wait by the moose antlers hanging on the wall.

We present Habitat, a platform for research in embodied artificial intelligence (AI). Habitat enables training embodied agents (virtual robots) in highly efficient photorealistic 3D simulation. Specifically, Habitat consists of: (i) Habitat-Sim: a flexible, high-performance 3D simulator with configurable agents, sensors, and generic 3D dataset handling. Habitat-Sim is fast -when rendering a scene from Matterport3D, it achieves several thousand frames per second (fps) running single-threaded, and can reach over 10,000 fps multi-process on a single GPU. (ii) Habitat-API: a modular high-level library for end-toend development of embodied AI algorithms -defining tasks (e.g. navigation, instruction following, question answering), configuring, training, and benchmarking embodied agents.These large-scale engineering contributions enable us to answer scientific questions requiring experiments that were till now impracticable or 'merely' impractical. Specifically, in the context of point-goal navigation: (1) we revisit the comparison between learning and SLAM approaches from two recent works [20,16] and find evidence for the opposite conclusion -that learning outperforms SLAM if scaled to an order of magnitude more experience than previous investigations, and (2) we conduct the first cross-dataset generalization experiments {train, test} × {Matterport3D, Gibson} for multiple sensors {blind, RGB, RGBD, D} and find that only agents with depth (D) sensors generalize across datasets. We hope that our open-source platform and these findings will advance research in embodied AI.

我们提出了一种可扩展的方法，用于学习开放世界对象目标导航（ObjectNAV） - 要求虚拟机器人（代理）在未探索的环境中找到对象的任何实例（例如，“查找接收器”）。我们的方法完全是零拍的 - 即，它不需要任何形式的objectNav奖励或演示。取而代之的是，我们训练图像目标导航（ImagenAv）任务，在该任务中，代理在其中找到了捕获图片（即目标图像）的位置。具体而言，我们将目标图像编码为多模式的语义嵌入空间，以在未注释的3D环境（例如HM3D）中以大规模训练语义目标导航（Senanticnav）代理。训练后，可以指示Semanticnav代理查找以自由形式的自然语言描述的对象（例如，“接收器”，“浴室水槽”等），通过将语言目标投射到相同的多模式，语义嵌入空间中。结果，我们的方法启用了开放世界的ObjectNAV。我们在三个ObjectNAV数据集（Gibson，HM3D和MP3D）上广泛评估了我们的代理商，并观察到成功的4.2％-20.0％的绝对改进。作为参考，这些收益与2020年至2021年Objectnav挑战赛竞争对手之间成功的5％改善相似或更好。在开放世界的环境中，我们发现我们的代理商可以概括为明确提到的房间（例如，“找到厨房水槽”）的复合说明，并且何时可以推断目标室（例如，”找到水槽和炉子”）。

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.

Training embodied agents in simulation has become mainstream for the embodied AI community. However, these agents often struggle when deployed in the physical world due to their inability to generalize to real-world environments. In this paper, we present Phone2Proc, a method that uses a 10-minute phone scan and conditional procedural generation to create a distribution of training scenes that are semantically similar to the target environment. The generated scenes are conditioned on the wall layout and arrangement of large objects from the scan, while also sampling lighting, clutter, surface textures, and instances of smaller objects with randomized placement and materials. Leveraging just a simple RGB camera, training with Phone2Proc shows massive improvements from 34.7% to 70.7% success rate in sim-to-real ObjectNav performance across a test suite of over 200 trials in diverse real-world environments, including homes, offices, and RoboTHOR. Furthermore, Phone2Proc's diverse distribution of generated scenes makes agents remarkably robust to changes in the real world, such as human movement, object rearrangement, lighting changes, or clutter.

我们介绍了栖息地2.0（H2.0），这是一个模拟平台，用于培训交互式3D环境和复杂物理的场景中的虚拟机器人。我们为体现的AI堆栈 - 数据，仿真和基准任务做出了全面的贡献。具体来说，我们提出：（i）复制：一个由艺术家的，带注释的，可重新配置的3D公寓（匹配真实空间）与铰接对象（例如可以打开/关闭的橱柜和抽屉）； （ii）H2.0：一个高性能物理学的3D模拟器，其速度超过8-GPU节点上的每秒25,000个模拟步骤（实时850x实时），代表先前工作的100倍加速；和（iii）家庭助理基准（HAB）：一套辅助机器人（整理房屋，准备杂货，设置餐桌）的一套常见任务，以测试一系列移动操作功能。这些大规模的工程贡献使我们能够系统地比较长期结构化任务中的大规模加固学习（RL）和经典的感官平面操作（SPA）管道，并重点是对新对象，容器和布局的概括。 。我们发现（1）与层次结构相比，（1）平面RL政策在HAB上挣扎； （2）具有独立技能的层次结构遭受“交接问题”的困扰，（3）水疗管道比RL政策更脆。

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.

A household robot should be able to navigate to target locations without requiring users to first annotate everything in their home. Current approaches to this object navigation challenge do not test on real robots and rely on expensive semantically labeled 3D meshes. In this work, our aim is an agent that builds self-supervised models of the world via exploration, the same as a child might. We propose an end-to-end self-supervised embodied agent that leverages exploration to train a semantic segmentation model of 3D objects, and uses those representations to learn an object navigation policy purely from self-labeled 3D meshes. The key insight is that embodied agents can leverage location consistency as a supervision signal - collecting images from different views/angles and applying contrastive learning to fine-tune a semantic segmentation model. In our experiments, we observe that our framework performs better than other self-supervised baselines and competitively with supervised baselines, in both simulation and when deployed in real houses.

从“Internet AI”的时代到“体现AI”的时代，AI算法和代理商出现了一个新兴范式转变，其中不再从主要来自Internet策划的图像，视频或文本的数据集。相反，他们通过与与人类类似的Enocentric感知来通过与其环境的互动学习。因此，对体现AI模拟器的需求存在大幅增长，以支持各种体现的AI研究任务。这种越来越多的体现AI兴趣是有利于对人工综合情报（AGI）的更大追求，但对这一领域并无一直存在当代和全面的调查。本文旨在向体现AI领域提供百科全书的调查，从其模拟器到其研究。通过使用我们提出的七种功能评估九个当前体现的AI模拟器，旨在了解模拟器，以其在体现AI研究和其局限性中使用。最后，本文调查了体现AI - 视觉探索，视觉导航和体现问题的三个主要研究任务（QA），涵盖了最先进的方法，评估指标和数据集。最后，随着通过测量该领域的新见解，本文将为仿真器 - 任务选择和建议提供关于该领域的未来方向的建议。

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/

移动机器人的视觉导航经典通过SLAM加上最佳规划，最近通过实现作为深网络的端到端培训。虽然前者通常仅限于航点计划，但即使在真实的物理环境中已经证明了它们的效率，后一种解决方案最常用于模拟中，但已被证明能够学习更复杂的视觉推理，涉及复杂的语义规则。通过实际机器人在物理环境中导航仍然是一个开放问题。端到端的培训方法仅在模拟中进行了彻底测试，实验涉及实际机器人的实际机器人在简化的实验室条件下限制为罕见的性能评估。在这项工作中，我们对真实物理代理的性能和推理能力进行了深入研究，在模拟中培训并部署到两个不同的物理环境。除了基准测试之外，我们提供了对不同条件下不同代理商培训的泛化能力的见解。我们可视化传感器使用以及不同类型信号的重要性。我们展示了，对于Pointgoal Task，一个代理在各种任务上进行预先培训，并在目标环境的模拟版本上进行微调，可以达到竞争性能，而无需建模任何SIM2重传，即通过直接从仿真部署培训的代理即可一个真正的物理机器人。

自主代理可以在新环境中导航而不构建明确的地图吗？对于PointGoal Navigation的任务（“转到$ \ delta x $，$ \ delta y $'），在理想化的设置（否RGB -D和驱动噪声，完美的GPS+Compass）下，答案是一个明确的“是” - 由任务无形组件（CNNS和RNN）组成的无地图神经模型接受了大规模增强学习训练，在标准数据集（Gibson）上取得了100％的成功。但是，对于PointNav在现实环境中（RGB-D和致动噪声，没有GPS+Compass），这是一个悬而未决的问题。我们在本文中解决了一个。该任务的最强成绩是成功的71.7％。首先，我们确定了性能下降的主要原因：GPS+指南针的缺失。带有RGB-D传感和致动噪声的完美GPS+指南针的代理商取得了99.8％的成功（Gibson-V2 Val）。这表明（解释模因）强大的视觉探子仪是我们对逼真的PointNav所需的全部。如果我们能够实现这一目标，我们可以忽略感应和致动噪声。作为我们的操作假设，我们扩展了数据集和模型大小，并开发了无人批准的数据启发技术来训练模型以进行视觉探测。我们在栖息地现实的PointNAV挑战方面的最新状态从71％降低到94％的成功（+23，31％相对）和53％至74％的SPL（+21，40％相对）。虽然我们的方法不饱和或“解决”该数据集，但这种强大的改进与有希望的零射击SIM2REAL转移（到Locobot）相结合提供了与假设一致的证据，即即使在现实环境中，显式映射也不是必需的。 。

对比语言图像预测（剪辑）编码器已被证明是有利于对分类和检测到标题和图像操纵的一系列视觉任务。我们调查剪辑视觉骨干网的有效性，以实现AI任务。我们构建令人难以置信的简单基线，名为Emplip，没有任务特定的架构，归纳偏差（如使用语义地图），培训期间的辅助任务，或深度映射 - 但我们发现我们的改进的基线在范围内表现得非常好任务和模拟器。 empclip将Robothor ObjectNav排行榜上面的20分的巨额边缘（成功率）。它使ithor 1相重新安排排行榜上面，击败了采用主动神经映射的下一个最佳提交，而且多于固定的严格度量（0.08至0.17）。它还击败了2021年栖息地对象挑战的获奖者，该挑战采用辅助任务，深度地图和人类示范以及2019年栖息地进程挑战的挑战。我们评估剪辑视觉表示在捕获有关输入观测的语义信息时的能力 - 用于导航沉重的体现任务的基元 - 并且发现剪辑的表示比想象成掠过的骨干更有效地编码这些基元。最后，我们扩展了我们的一个基线，产生了能够归零对象导航的代理，该导航可以导航到在训练期间未被用作目标的对象。

We present the Habitat-Matterport 3D Semantics (HM3DSEM) dataset. HM3DSEM is the largest dataset of 3D real-world spaces with densely annotated semantics that is currently available to the academic community. It consists of 142,646 object instance annotations across 216 3D spaces and 3,100 rooms within those spaces. The scale, quality, and diversity of object annotations far exceed those of prior datasets. A key difference setting apart HM3DSEM from other datasets is the use of texture information to annotate pixel-accurate object boundaries. We demonstrate the effectiveness of HM3DSEM dataset for the Object Goal Navigation task using different methods. Policies trained using HM3DSEM perform outperform those trained on prior datasets. Introduction of HM3DSEM in the Habitat ObjectNav Challenge lead to an increase in participation from 400 submissions in 2021 to 1022 submissions in 2022.

在本文中，我们专注于在线学习主动视觉在未知室内环境中的对象的搜索（AVS）的最优策略问题。我们建议POMP++，规划战略，介绍了经典的部分可观察蒙特卡洛规划（POMCP）框架之上的新制剂，允许免费培训，在线政策在未知的环境中学习。我们提出了一个新的信仰振兴战略，允许使用POMCP与动态扩展状态空间来解决在线生成平面地图的。我们评估我们在两个公共标准数据集的方法，AVD由是从真正的3D场景渲染扫描真正的机器人平台和人居ObjectNav收购，用>10％，比国家的the-改善达到最佳的成功率技术方法。

我们介绍了泰德（Tidee），这是一种体现的代理，它根据学识渊博的常识对象和房间安排先验来整理一个无序场景。泰德（Tidee）探索家庭环境，检测到其自然位置的对象，渗透到它们的合理对象上下文，在当前场景中定位此类上下文，并重新定位对象。常识先验在三个模块中编码：i）检测到现象对象的视觉声音检测器，ii）对象和空间关系的关联神经图记忆，提出了对象重新定位的合理语义插座和表面，以及iii）引导代理商探索的可视搜索网络，以有效地将利益定位在当前场景中以重新定位对象。我们测试了在AI2THOR模拟环境中整理混乱的场景的潮汐。 Tidee直接从像素和原始深度输入中执行任务，而没有事先观察到同一房间，仅依靠从单独的一组培训房屋中学到的先验。人类对由此产生的房间进行重组的评估表明，泰德（Tidee）的表现优于该模型的消融版本，这些版本不使用一个或多个常识性先验。在相关的房间重新安排基准测试中，该基准使代理可以在重新排列前查看目标状态，我们的模型的简化版本大大胜过了最佳的方法，可以通过大幅度的差距。代码和数据可在项目网站上获得：https：//tidee-agent.github.io/。

We present a new AI task -Embodied Question Answering (EmbodiedQA) -where an agent is spawned at a random location in a 3D environment and asked a question ('What color is the car?'). In order to answer, the agent must first intelligently navigate to explore the environment, gather information through first-person (egocentric) vision, and then answer the question ('orange'). This challenging task requires a range of AI skills -active perception, language understanding, goal-driven navigation, commonsense reasoning, and grounding of language into actions. In this work, we develop the environments, end-to-end-trained reinforcement learning agents, and evaluation protocols for EmbodiedQA.

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.

For robots to be generally useful, they must be able to find arbitrary objects described by people (i.e., be language-driven) even without expensive navigation training on in-domain data (i.e., perform zero-shot inference). We explore these capabilities in a unified setting: language-driven zero-shot object navigation (L-ZSON). Inspired by the recent success of open-vocabulary models for image classification, we investigate a straightforward framework, CLIP on Wheels (CoW), to adapt open-vocabulary models to this task without fine-tuning. To better evaluate L-ZSON, we introduce the Pasture benchmark, which considers finding uncommon objects, objects described by spatial and appearance attributes, and hidden objects described relative to visible objects. We conduct an in-depth empirical study by directly deploying 21 CoW baselines across Habitat, RoboTHOR, and Pasture. In total, we evaluate over 90k navigation episodes and find that (1) CoW baselines often struggle to leverage language descriptions, but are proficient at finding uncommon objects. (2) A simple CoW, with CLIP-based object localization and classical exploration -- and no additional training -- matches the navigation efficiency of a state-of-the-art ZSON method trained for 500M steps on Habitat MP3D data. This same CoW provides a 15.6 percentage point improvement in success over a state-of-the-art RoboTHOR ZSON model.