Robot world model representations are a vital part of robotic applications. However, there is no support for such representations in model-driven engineering tool chains. This work proposes a novel Domain Specific Language (DSL) for robotic world models that are based on the Robot Scene Graph (RSG) approach. The RSG-DSL can express (a) application specific scene configurations, (b) semantic scene structures and (c) inputs and outputs for the computational entities that are loaded into an instance of a world model.
Autonomous robotic grasping plays an important role in intelligent robotics. However, how to help the robot grasp specific objects in object stacking scenes is still an open problem, because there are two main challenges for autonomous robots: (1)it is a comprehensive task to know what and how to grasp; (2)it is hard to deal with the situations in which the target is hidden or covered by other objects. In this paper, we propose a multi-task convolutional neural network for autonomous robotic grasping, which can help the robot find the target, make the plan for grasping and finally grasp the ta…
NVIDIA（英伟达）；Georgia Institute of Technology（佐治亚理工学院）
We present a system for multi-level scene awareness for robotic manipulation. Given a sequence of camera-in-hand RGB images, the system calculates three types of information: 1) a point cloud representation of all the surfaces in the scene, for the purpose of obstacle avoidance; 2) the rough pose of unknown objects from categories corresponding to primitive shapes (e.g., cuboids and cylinders); and 3) full 6-DoF pose of known objects. By developing and fusing recent techniques in these domains, we provide a rich scene representation for robot awareness. We demonstrate the importance of each of…
Australian Research Council Centre of Excellence for Robotic Vision（澳大利亚研究理事会机器人视觉卓越中心）；QUT Centre for Robotics（昆士兰科技大学机器人中心）
Being able to explore an environment and understand the location and type of all objects therein is important for indoor robotic platforms that must interact closely with humans. However, it is difficult to evaluate progress in this area due to a lack of standardized testing which is limited due to the need for active robot agency and perfect object ground-truth. To help provide a standard for testing scene understanding systems, we present a new robot vision scene understanding challenge using simulation to enable repeatable experiments with active robot agency. We provide two challenging tas…
Grasp detection with consideration of the affiliations between grasps and their owner in object overlapping scenes is a necessary and challenging task for the practical use of the robotic grasping approach. In this paper, a robotic grasp detection algorithm named ROI-GD is proposed to provide a feasible solution to this problem based on Region of Interest (ROI), which is the region proposal for objects. ROI-GD uses features from ROIs to detect grasps instead of the whole scene. It has two stages: the first stage is to provide ROIs in the input image and the second-stage is the grasp detector b…
Applied Ocean Physics and Engineering Department at the Woods Hole Oceanographic Institution (WHOI)（伍兹霍尔海洋研究所（WHOI）应用海洋物理与工程系）；MIT Media Lab（麻省理工学院媒体实验室）；MIT-WHOI Joint Program in Applied Ocean Science and Engineering（麻省理工学院-伍兹霍尔海洋研究所应用海洋科学与工程联合项目）；Mechanical Engineering Department at Northeastern University（东北大学机械工程系）
This paper proposes a bandwidth tunable technique for real-time probabilistic scene modeling and mapping to enable co-robotic exploration in communication constrained environments such as the deep sea. The parameters of the system enable the user to characterize the scene complexity represented by the map, which in turn determines the bandwidth requirements. The approach is demonstrated using an underwater robot that learns an unsupervised scene model of the environment and then uses this scene model to communicate the spatial distribution of various high-level semantic scene constructs to a h…
AeroScene: Progressive Scene Synthesis for Aerial Robotics
University of Liverpool, UK（英国利物浦大学）；AIOZ Ltd., Singapore（新加坡AIOZ有限公司）；National Tsing Hua University, Taiwan（台湾国立清华大学）；RMIT University, Vietnam Campus（越南RMIT大学校园）；University of Information Technology, VNUHCM, Vietnam（越南VNUHCM大学信息科技学院）
Where to Fetch: Extracting Visual Scene Representation from Large Pre-Trained Models for Robotic Goal Navigation
School of Control Science and Engineering, Shandong University（山东大学控制科学与工程学院）；Campus Security Smart Brain Shandong Engineering Research Center（山东校园安全智能脑工程研究中心）
HyperDCM: Dynamic Cluster Memory Replay in Hyperbolic Space for Continual Robotic Navigation Across Scenes
Software Engineering Institute, East China Normal University（华东师范大学软件工程学院）；School of Geospatial Information, Information Engineering University（信息工程大学地理空间信息学院）；University of Shanghai for Science and Technology（上海理工大学）；Shanghai Jiao Tong University（上海交通大学）
Fudan University（复旦大学）；Westlake University（西湖大学）；Australian National University（澳大利亚国立大学）；Westlake Institute for Advanced Study（西湖高等研究院）
Current NLP techniques have been greatly applied in different domains. In this paper, we propose a human-in-the-loop framework for robotic grasping in cluttered scenes, investigating a language interface to the grasping process, which allows the user to intervene by natural language commands. This framework is constructed on a state-of-the-art rasping baseline, where we substitute a scene-graph representation with a text representation of the scene using BERT. Experiments on both simulation and physical robot show that the proposed method outperforms conventional object-agnostic and scene-grap…
Accurate knowledge of object poses is crucial to successful robotic manipulation tasks, and yet most current approaches only work in laboratory settings. Noisy sensors and cluttered scenes interfere with accurate pose recognition, which is problematic especially when performing complex tasks involving object interactions. This is because most pose estimation algorithms focus only on estimating objects from a single frame, which means they lack continuity between frames. Further, they often do not consider resulting physical properties of the predicted scene such as intersecting objects or obje…
THUD++: Large-Scale Dynamic Indoor Scene Dataset and Benchmark for Mobile Robots
Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）
Robotic control tasks are often first run in simulation for the purposes of verification, debugging and data augmentation. Many methods exist to specify what task a robot must complete, but few exist to specify what range of environments a user expects such tasks to be achieved in. ProbRobScene is a probabilistic specification language for describing robotic manipulation environments. Using the language, a user need only specify the relational constraints that must hold between objects in a scene. ProbRobScene will then automatically generate scenes which conform to this specification. By comb…
Rochester Institute of Technology（罗切斯特理工学院）；OSARO Inc（OSARO公司）；Siemens Corporation, Corporate Technology（西门子公司企业技术部）
In this work, we focus on multi-step manipulation tasks that involve long-horizon planning and considers progress reversal. Such tasks interlace high-level reasoning that consists of the expected states that can be attained to achieve an overall task and low-level reasoning that decides what actions will yield these states. We propose a sample efficient Previous Action Conditioned Robotic Manipulation Network (PAC-RoManNet) to learn the action-value functions and predict manipulation action candidates from visual observation of the scene and action-value predictions of the previous action. We …
Universidad Pablo de Olavide（巴勃罗·德·奥拉维德大学）；Universidad de Sevilla（塞维利亚大学）；Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI)（安达卢西亚数据科学与计算智能研究所（DaSCI））；University of Granada（格拉纳达大学）
Scene graph generation from images is a task of great interest to applications such as robotics, because graphs are the main way to represent knowledge about the world and regulate human-robot interactions in tasks such as Visual Question Answering (VQA). Unfortunately, its corresponding area of machine learning is still relatively in its infancy, and the solutions currently offered do not specialize well in concrete usage scenarios. Specifically, they do not take existing "expert" knowledge about the domain world into account; and that might indeed be necessary in order to provide the level o…
University of Toronto Institute for Aerospace Studies（多伦多大学航空航天研究所）；Robotics Institute（机器人研究所）；Epson Canada（爱普生加拿大公司）
In robotic bin-picking applications, the perception of texture-less, highly reflective parts is a valuable but challenging task. The high glossiness can introduce fake edges in RGB images and inaccurate depth measurements especially in heavily cluttered bin scenario. In this paper, we present the ROBI (Reflective Objects in BIns) dataset, a public dataset for 6D object pose estimation and multi-view depth fusion in robotic bin-picking scenarios. The ROBI dataset includes a total of 63 bin-picking scenes captured with two active stereo camera: a high-cost Ensenso sensor and a low-cost RealSense…
Guangzhou University（广州大学）；University of Lincoln（林肯大学）
Visual dynamic complexity is a ubiquitous, hidden attribute of the visual world that every dynamic vision system is faced with. However, it is implicit and intractable which has never been quantitatively described due to the difficulty in defending temporal features correlated to spatial image complexity. To fill this vacancy, we propose a novel bio-robotic approach to profile visual dynamic complexity which can be used as a new metric. Here we apply a state-of-the-art brain-inspired motion detection neural network model to explicitly profile such complexity associated with spatial-temporal fr…
Interestingness recognition is crucial for decision making in autonomous exploration for mobile robots. Previous methods proposed an unsupervised online learning approach that can adapt to environments and detect interesting scenes quickly, but lack the ability to adapt to human-informed interesting objects. To solve this problem, we introduce a human-interactive framework, AirInteraction, that can detect human-informed objects via few-shot online learning. To reduce the communication bandwidth, we first apply an online unsupervised learning algorithm on the unmanned vehicle for interestingnes…
You Only Scan Once: A Dynamic Scene Reconstruction Pipeline for 6-DoF Robotic Grasping of Novel Objects
Advanced Robotics Centre, National University of Singapore（新加坡国立大学先进机器人中心）；Integrative Sciences and Engineering Programme, National University of Singapore Graduate School（新加坡国立大学研究生院整合科学与工程项目）
Hi-Dyna Graph: Hierarchical Dynamic Scene Graph for Robotic Autonomy in Human-Centric Environments
School of Computer Science, Fudan University（复旦大学计算机科学学院）；Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University（复旦大学脑启发式智能科学研究院）
The reliability of grasp detection for target objects in complex scenes is a challenging task and a critical problem that needs to be solved urgently in practical application. At present, the grasp detection location comes from searching the feature space of the whole image. However, the cluttered background information in the image impairs the accuracy of grasping detection. In this paper, a robotic grasp detection algorithm named MASK-GD is proposed, which provides a feasible solution to this problem. MASK is a segmented image that only contains the pixels of the target object. MASK-GD for g…
Humans form mental images of 3D scenes to support counterfactual imagination, planning, and motor control. Our abilities to predict the appearance and affordance of the scene from previously unobserved viewpoints aid us in performing manipulation tasks (e.g., 6-DoF kitting) with a level of ease that is currently out of reach for existing robot learning frameworks. In this work, we aim to build artificial systems that can analogously plan actions on top of imagined images. To this end, we introduce Mental Imagery for Robotic Affordances (MIRA), an action reasoning framework that optimizes actio…
Preference-Driven Active 3D Scene Representation for Robotic Inspection in Nuclear Decommissioning
School of Computing Science, University of Glasgow（格拉斯哥大学计算科学学院）；University of Manchester（曼彻斯特大学）；James Watt School of Engineering, University of Glasgow（格拉斯哥大学詹姆斯·瓦特工程学院）
MSGField: A Unified Scene Representation Integrating Motion, Semantics, and Geometry for Robotic Manipulation
School of Computer Science and Technology, University of Science and Technology of China (USTC)（中国科学技术大学计算机科学与技术学院）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合性国家科学中心人工智能研究院）
Generating Actionable Robot Knowledge Bases by Combining 3D Scene Graphs with Robot Ontologies
Institute for Artificial Intelligence, University of Bremen, Germany（人工智能研究所，不莱梅大学）；Appliable Linguistics, University of Bremen, Germany（应用语言学，不莱梅大学）；Automation & Control Institute, TU Wien, Austria（自动化与控制研究所，维也纳技术大学）
University of applied Sciences Augsburg（奥格斯堡应用技术大学）
Adapting robot programmes to changes in the environment is a well-known industry problem, and it is the reason why many tedious tasks are not automated in small and medium-sized enterprises (SMEs). A semantic world model of a robot's previously unknown environment created from point clouds is one way for these companies to automate assembly tasks that are typically performed by humans. The semantic segmentation of point clouds for robot manipulators or cobots in industrial environments has received little attention due to a lack of suitable datasets. This paper describes a pipeline for creatin…
University of Bremen（不来梅大学）；Örebro University（厄勒布鲁大学）
We present a commonsense, qualitative model for the semantic grounding of embodied visuo-spatial and locomotive interactions. The key contribution is an integrative methodology combining low-level visual processing with high-level, human-centred representations of space and motion rooted in artificial intelligence. We demonstrate practical applicability with examples involving object interactions, and indoor movement.
We present four different robust transfer learning and data augmentation strategies for robust mobile scene recognition. By training three mobile-ready (EfficientNetB0, MobileNetV2, MobileNetV3) and two large-scale baseline (VGG16, ResNet50) convolutional neural network architectures on the widely available Event8, Scene15, Stanford40, and MIT67 datasets, we show the generalization ability of our transfer learning strategies. Furthermore, we tested the robustness of our transfer learning strategies under viewpoint and lighting changes using the KTH-Idol2 database. Also, the impact of inference…
University of Toronto Institute for Aerospace Studies（多伦多大学航空航天研究所）；Robotics Institute（机器人研究所）；Epson Canada Ltd.（爱普生加拿大有限公司）
The reliable fusion of depth maps from multiple viewpoints has become an important problem in many 3D reconstruction pipelines. In this work, we investigate its impact on robotic bin-picking tasks such as 6D object pose estimation. The performance of object pose estimation relies heavily on the quality of depth data. However, due to the prevalence of shiny surfaces and cluttered scenes, industrial grade depth cameras often fail to sense depth or generate unreliable measurements from a single viewpoint. To this end, we propose a novel probabilistic framework for scene reconstruction in robotic …
Continual Hand-Eye Calibration for Open-world Robotic Manipulation
School of Automation Science and Engineering, South China University of Technology（华南理工大学自动化科学与工程学院）；State Key Laboratory of Robotics, the Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences（中国科学院机器人研究所）；University of Chinese Academy of Sciences（中国科学院大学）
From Pixels to Concepts: Growing Rich 3D Semantic Scene Graph Forests utilizing Foundation Models
FZI Research Center for Information Technology（FZI信息技术研究中心）；Machine Intelligence and Robotics Lab (MaiRo), Karlsruhe Institute for Technology (KIT)（卡尔斯鲁厄理工学院机器智能与机器人实验室）
