Vision-and-Language Navigation (VLN) requires an agent to follow natural-language instructions, explore the given environments, and reach the desired target locations. These step-by-step navigational instructions are crucial when the agent is navigating new environments about which it has no prior knowledge. Most recent works that study VLN observe a significant performance drop when tested on unseen environments (i.e., environments not used in training), indicating that the neural agent models are highly biased towards training environments. Although this issue is considered as one of the major challenges in VLN research, it is still under-studied and needs a clearer explanation. In this work, we design novel diagnosis experiments via environment re-splitting and feature replacement, looking into possible reasons for this environment bias. We observe that neither the language nor the underlying navigational graph, but the low-level visual appearance conveyed by ResNet features directly affects the agent model and contributes to this environment bias in results. According to this observation, we explore several kinds of semantic representations that contain less low-level visual information, hence the agent learned with these features could be better generalized to unseen testing environments. Without modifying the baseline agent model and its training method, our explored semantic features significantly decrease the performance gaps between seen and unseen on multiple datasets (i.e. R2R, R4R, and CVDN) and achieve competitive unseen results to previous state-of-the-art models. Our code and features are available at: https://github.com/zhangybzbo/EnvBiasVLN
UC Santa Barbara（加州大学圣巴巴拉分校）；University of Adelaide（阿德莱德大学）；Google（谷歌）；UC Santa Cruz（加州大学圣克鲁兹分校）
Vision-and-language navigation (VLN) is a multimodal task where an agent follows natural language instructions and navigates in visual environments. Multiple setups have been proposed, and researchers apply new model architectures or training techniques to boost navigation performance. However, there still exist non-negligible gaps between machines' performance and human benchmarks. Moreover, the agents' inner mechanisms for navigation decisions remain unclear. To the best of our knowledge, how the agents perceive the multimodal input is under-studied and needs investigation. In this work, we …
An embodied task such as embodied question answering (EmbodiedQA), requires an agent to explore the environment and collect clues to answer a given question that related with specific objects in the scene. The solution of such task usually includes two stages, a navigator and a visual Q&A module. In this paper, we focus on the navigation and solve the problem of existing navigation algorithms lacking experience and common sense, which essentially results in a failure finding target when robot is spawn in unknown environments. Inspired by the human ability to think twice before moving and conce…
Vision and language navigation (VLN) is a challenging visually-grounded language understanding task. Given a natural language navigation instruction, a visual agent interacts with a graph-based environment equipped with panorama images and tries to follow the described route. Most prior work has been conducted in indoor scenarios where best results were obtained for navigation on routes that are similar to the training routes, with sharp drops in performance when testing on unseen environments. We focus on VLN in outdoor scenarios and find that in contrast to indoor VLN, most of the gain in ou…
We introduce Room-Across-Room (RxR), a new Vision-and-Language Navigation (VLN) dataset. RxR is multilingual (English, Hindi, and Telugu) and larger (more paths and instructions) than other VLN datasets. It emphasizes the role of language in VLN by addressing known biases in paths and eliciting more references to visible entities. Furthermore, each word in an instruction is time-aligned to the virtual poses of instruction creators and validators. We establish baseline scores for monolingual and multilingual settings and multitask learning when including Room-to-Room annotations. We also provid…
The exponentially increasing advances in robotics and machine learning are facilitating the transition of robots from being confined to controlled industrial spaces to performing novel everyday tasks in domestic and urban environments. In order to make the presence of robots safe as well as comfortable for humans, and to facilitate their acceptance in public environments, they are often equipped with social abilities for navigation and interaction. Socially compliant robot navigation is increasingly being learned from human observations or demonstrations. We argue that these techniques that ty…
Object navigation tasks require agents to locate specific objects in unknown environments based on visual information. Previously, graph convolutions were used to implicitly explore the relationships between objects. However, due to differences in visibility among objects, it is easy to generate biases in object attention. Thus, in this paper, we propose a directed object attention (DOA) graph to guide the agent in explicitly learning the attention relationships between objects, thereby reducing the object attention bias. In particular, we use the DOA graph to perform unbiased adaptive object …
Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Monash University（莫纳什大学）；RMIT University（皇家墨尔本理工大学）；Sun Yat-sen University（中山大学）
Vision-language Navigation (VLN) tasks require an agent to navigate step-by-step while perceiving the visual observations and comprehending a natural language instruction. Large data bias, which is caused by the disparity ratio between the small data scale and large navigation space, makes the VLN task challenging. Previous works have proposed various data augmentation methods to reduce data bias. However, these works do not explicitly reduce the data bias across different house scenes. Therefore, the agent would overfit to the seen scenes and achieve poor navigation performance in the unseen …
Ask When It Pays: Cost-Aware Open-Ended Interaction for Instance Goal Navigation
Adelaide University（阿德莱德大学）；Responsible AI Research Centre, Australian Institute for Machine Learning（负责任人工智能研究中心，澳大利亚机器学习研究所）；Institute for Infocomm Research (I2R), A*STAR（信息与通信研究院（I2R），A*STAR）；iMotion；CSIRO Data61 Project Website（CSIRO Data61项目网站）
UltraStar: Semantic-Aware Star Graph Modeling for Echocardiography Navigation
Department of Automation, BNRist, Tsinghua University, Beijing, China（自动化系、BNRist、清华大学、北京、中国）；School of Automation, Beijing Institute of Technology, Beijing, China（自动化学院、北京理工大学、北京、中国）；Beijing Academy of Artificial Intelligence, Beijing, China（北京人工智能研究院、北京、中国）；Chinese PLA General Hospital, Beijing, China（中国人民解放军总医院、北京、中国）
UC Santa Barbara（加州大学圣巴巴拉分校）；McGill University（麦吉尔大学）；Peking University（北京大学）；UC Santa Cruz（加州大学圣克鲁兹分校）
Vision-Language Navigation requires the agent to follow natural language instructions to reach a specific target. The large discrepancy between seen and unseen environments makes it challenging for the agent to generalize well. Previous studies propose data augmentation methods to mitigate the data bias explicitly or implicitly and provide improvements in generalization. However, they try to memorize augmented trajectories and ignore the distribution shifts under unseen environments at test time. In this paper, we propose an Unseen Discrepancy Anticipating Vision and Language Navigation (DAVIS…
We introduce and show preliminary results of a fast randomized method that finds a set of K paths lying in distinct homotopy classes. We frame the path planning task as a graph search problem, where the navigation graph is based on a Voronoi diagram. The search is biased by a cost function derived from the social force model that is used to generate and select the paths. We compare our method to Yen's algorithm, and empirically show that our approach is faster to find a subset of homotopy classes. Furthermore our approach computes a set of more diverse paths with respect to the baseline while …
Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区）；Monash University（莫纳什大学）；Alibaba group（阿里巴巴集团）
The vision-language navigation (VLN) task requires an agent to reach a target with the guidance of natural language instruction. Previous works learn to navigate step-by-step following an instruction. However, these works may fail to discriminate the similarities and discrepancies across instruction-trajectory pairs and ignore the temporal continuity of sub-instructions. These problems hinder agents from learning distinctive vision-and-language representations, harming the robustness and generalizability of the navigation policy. In this paper, we propose a Contrastive Instruction-Trajectory L…
Autonomous Robots Lab, University of Nevada, Reno（内华达大学雷诺分校自主机器人实验室）
This paper proposes a method for tight fusion of visual, depth and inertial data in order to extend robotic capabilities for navigation in GPS-denied, poorly illuminated, and texture-less environments. Visual and depth information are fused at the feature detection and descriptor extraction levels to augment one sensing modality with the other. These multimodal features are then further integrated with inertial sensor cues using an extended Kalman filter to estimate the robot pose, sensor bias terms, and landmark positions simultaneously as part of the filter state. As demonstrated through a s…
Georgia Institute of Technology（佐治亚理工学院）；FAIR, Meta AI（元人工智能基础人工智能研究院）；Simon Fraser University（西蒙弗雷泽大学）；Google Research Atlanta（谷歌亚特兰大研究院）；Oregon State University（俄勒冈州立大学）
Animal navigation research posits that organisms build and maintain internal spatial representations, or maps, of their environment. We ask if machines -- specifically, artificial intelligence (AI) navigation agents -- also build implicit (or 'mental') maps. A positive answer to this question would (a) explain the surprising phenomenon in recent literature of ostensibly map-free neural-networks achieving strong performance, and (b) strengthen the evidence of mapping as a fundamental mechanism for navigation by intelligent embodied agents, whether they be biological or artificial. Unlike animal…
Getting Sick After Seeing a Doctor? Diagnosing and Mitigating Knowledge Conflicts in Event Temporal Reasoning
Hong Kong University of Science and Technology（香港科技大学）；University of Southern California（南加州大学）；Tencent AI Lab（腾讯AI实验室）；University of California, Davis（加州大学戴维斯分校）
Entity typing aims at predicting one or more words that describe the type(s) of a specific mention in a sentence. Due to shortcuts from surface patterns to annotated entity labels and biased training, existing entity typing models are subject to the problem of spurious correlations. To comprehensively investigate the faithfulness and reliability of entity typing methods, we first systematically define distinct kinds of model biases that are reflected mainly from spurious correlations. Particularly, we identify six types of existing model biases, including mention-context bias, lexical overlapp…
C2PO: Diagnosing and Disentangling Bias Shortcuts in LLMs
Jinan University（济南大学）；Nanyang Technological University（南洋理工大学）；Engineering Research Center of Trustworthy AI (Ministry of Education)（可信人工智能工程研究中心）；Guangxi Key Laboratory of Trusted Software（广西可信软件重点实验室）
Diagnosing Retrieval Bias Under Multiple In-Context Knowledge Updates in Large Language Models
Institute of Information Engineering, Chinese Academy of Sciences（信息工程研究所，中国科学院）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络与安全学院）；Hong Kong University of Science and Technology（香港科学与技术大学）；The University of Manchester（曼彻斯特大学）
The Sword, Shield, and Achilles' Heel: Characterizing the Linguistic Inductive Bias of Large Language Models for Spatial Reasoning in Navigation Planning
East China Normal University（东华大学）；Information Engineering University（信息工程大学）；Zhengzhou University（郑州大学）
For embodied agents, navigation is an important ability but not an isolated goal. Agents are also expected to perform specific tasks after reaching the target location, such as picking up objects and assembling them into a particular arrangement. We combine Vision-and-Language Navigation, assembling of collected objects, and object referring expression comprehension, to create a novel joint navigation-and-assembly task, named ArraMon. During this task, the agent (similar to a PokeMON GO player) is asked to find and collect different target objects one-by-one by navigating based on natural lang…
Key Lab of Intell. Info. Process., Inst. of Comput. Tech., CAS（中国科学院计算技术研究所智能信息处理重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）；Kingsoft Cloud（金山云）；Peng Cheng Laboratory（鹏城实验室）；Huawei Technologies（华为技术有限公司）
Language bias is a critical issue in Visual Question Answering (VQA), where models often exploit dataset biases for the final decision without considering the image information. As a result, they suffer from performance drop on out-of-distribution data and inadequate visual explanation. Based on experimental analysis for existing robust VQA methods, we stress the language bias in VQA that comes from two aspects, i.e., distribution bias and shortcut bias. We further propose a new de-bias framework, Greedy Gradient Ensemble (GGE), which combines multiple biased models for unbiased base model lea…
