Towards An Architecture-Centric Approach to Manage Variability of Cloud Robotics
Northeastern University（东北大学）；Université de Picardie Jules Verne（皮卡第儒勒-凡尔纳大学）；Huazhong University of Science and Technology（华中科技大学）；CNRS（法国国家科学研究中心）；Université Montpellier 2（蒙彼利埃第二大学）
Cloud robotics is a field of robotics that attempts to invoke Cloud technologies such as Cloud computing, Cloud storage, and other Internet technologies centered around the benefits of converged infrastructure and shared services for robotics. In a few short years, Cloud robotics as a newly emerged field has already received much research and industrial attention. The use of the Cloud for robotics and automation brings some potential benefits largely ameliorating the performance of robotic systems. However, there are also some challenges. First of all, from the viewpoint of architecture, how to model and describe the architectures of Cloud robotic systems? How to manage the variability of Cloud robotic systems? How to maximize the reuse of their architectures? In this paper, we present an architecture approach to easily design and understand Cloud robotic systems and manage their variability.
Due to the high dimensionality of object states, a garment flattening pipeline requires recognising the configurations of garments for a robot to produce/select manipulation plans to flatten garments. In this paper, we propose a data-centric approach to identify known configurations of garments based on a known configuration network (KCNet) trained on depth images that capture the known configurations of garments and prior knowledge of garment shapes. In this paper, we propose a data-centric approach to identify the known configurations of garments based on a known configuration network (KCNet…
STARRY: Spatial-Temporal Action-Centric World Modeling for Robotic Manipulation
Beijing Institute of Technology（北京理工大学）；Zhongguancun Academy（中关村学院）；Zhongguancun Institute of Artificial Intelligence（中关村人工智能研究院）；University of Science and Technology of China（中国科学技术大学）；Harbin Institute of Technology（哈尔滨工业大学）；East China Normal University（华东师范大学）；DeepCybo
We present RCareWorld, a human-centric simulation world for physical and social robotic caregiving designed with inputs from stakeholders, including care recipients, caregivers, occupational therapists, and roboticists. RCareWorld has realistic human models of care recipients with mobility limitations and caregivers, home environments with multiple levels of accessibility and assistive devices, and robots commonly used for caregiving. It interfaces with various physics engines to model diverse material types necessary for simulating caregiving scenarios, and provides the capability to plan, co…
This paper introduces Amazon Robotic Manipulation Benchmark (ARMBench), a large-scale, object-centric benchmark dataset for robotic manipulation in the context of a warehouse. Automation of operations in modern warehouses requires a robotic manipulator to deal with a wide variety of objects, unstructured storage, and dynamically changing inventory. Such settings pose challenges in perceiving the identity, physical characteristics, and state of objects during manipulation. Existing datasets for robotic manipulation consider a limited set of objects or utilize 3D models to generate synthetic sce…
Unifying Object-Centric World Models and Diffusion Policy: A Hierarchical Framework for Multi-Stage Robotic Tasks
Tandon School of Engineering, New York University（纽约大学坦登工程学院）；Courant Institute of Mathematical Sciences, New York University（纽约大学库朗数学科学研究所）；AMI Labs（AMI实验室）
Manipulation tasks can often be decomposed into multiple subtasks performed in parallel, e.g., sliding an object to a goal pose while maintaining contact with a table. Individual subtasks can be achieved by task-axis controllers defined relative to the objects being manipulated, and a set of object-centric controllers can be combined in an hierarchy. In prior works, such combinations are defined manually or learned from demonstrations. By contrast, we propose using reinforcement learning to dynamically compose hierarchical object-centric controllers for manipulation tasks. Experiments in both …
Robots Pre-train Robots: Manipulation-Centric Robotic Representation from Large-Scale Robot Datasets
University of California, San Diego（加利福尼亚大学圣迭戈分校）；Tongji University（同济大学）；Shanghai Jiao Tong University（上海交通大学）；University of Maryland, College Park（马里兰大学帕克分校）；Tsinghua University（清华大学）
Object-Centric Instruction Augmentation for Robotic Manipulation
School of Computer Science, East China Normal University（华东师范大学计算机科学学院）；Midea Group（美的集团）；Department of Mathematics, School of Science, Shanghai University（上海大学理学院数学系）
SlotVLA: Towards Modeling of Object-Relation Representations in Robotic Manipulation
University of Arkansas（亚拉巴马大学）；FPT Software AI Center（FPT软件人工智能中心）；University of Stuttgart（斯图加特大学）；Aalborg University（奥尔堡大学）；Carnegie Mellon University（卡内基梅隆大学）；University of Liverpool（利物浦大学）；German Research Center for Artificial Intelligence（德国人工智能研究中心）；Max Planck Research School for Intelligent Systems（马克斯·普朗克智能系统研究学校）
EC-Flow: Enabling Versatile Robotic Manipulation from Action-Unlabeled Videos via Embodiment-Centric Flow
New Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences（模式识别新实验室（NLPR），自动化研究所，中国科学院）；School of Artificial Intelligence, University of Chinese Academy of Sciences（人工智能学院，中国科学院大学）
Passivity-Centric Safe Reinforcement Learning for Contact-Rich Robotic Tasks
Human-Robot Interfaces and Interaction Lab, Istituto Italiano di Tecnologia, Genoa, Italy（人机交互实验室，意大利理工学院，热那亚，意大利）；Ph.D. program of national interest in Robotics and Intelligent Machines (DRIM) and Università di Genova（机器人与智能机器国家级博士项目（DRIM）和热那亚大学）
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）
Using a robotic platform for telepresence applications has gained paramount importance in this decade. Scenarios such as remote meetings, group discussions, and presentations/talks in seminars and conferences get much attention in this regard. Though there exist some robotic platforms for such telepresence applications, they lack efficacy in communication and interaction between the remote person and the avatar robot deployed in another geographic location. Also, such existing systems are often cloud-centric which adds to its network overhead woes. In this demo, we develop and test a framework…
Escaping the Diversity Trap in Robotic Manipulation via Anchor-Centric Adaptation
National University of Singapore（新加坡国立大学）；Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））；Institude for Information Research, A STAR（A*STAR信息研究所）
Decoupled Object-Centric Video Understanding for Generating Robotic Manipulation Commands
School of Information Science, Japan Advanced Institute of Science and Technology（日本北陆先端科学技术大学院大学信息科学学院）；University of Engineering and Technology, Vietnam National University（越南国立大学工程与技术大学）；Department of Robotics, Hanyang University（汉阳大学机器人学系）
Samsung AI Center（三星人工智能中心）；Princeton University（普林斯顿大学）
Being able to grasp objects is a fundamental component of most robotic manipulation systems. In this paper, we present a new approach to simultaneously reconstruct a mesh and a dense grasp quality map of an object from a depth image. At the core of our approach is a novel camera-centric object representation called the "object shell" which is composed of an observed "entry image" and a predicted "exit image". We present an image-to-image residual ConvNet architecture in which the object shell and a grasp-quality map are predicted as separate output channels. The main advantage of the shell rep…
The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Facebook AI Research（Facebook人工智能研究院）
Dexterous robotic hands are appealing for their agility and human-like morphology, yet their high degree of freedom makes learning to manipulate challenging. We introduce an approach for learning dexterous grasping. Our key idea is to embed an object-centric visual affordance model within a deep reinforcement learning loop to learn grasping policies that favor the same object regions favored by people. Unlike traditional approaches that learn from human demonstration trajectories (e.g., hand joint sequences captured with a glove), the proposed prior is object-centric and image-based, allowing …
Uni-Skill: Building Self-Evolving Skill Repository for Generalizable Robotic Manipulation
Key Laboratory of AI Safety of CAS（中国科学院人工智能安全重点实验室）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）
