Ordering the selection of training data using active learning can lead to improvements in learning efficiently from smaller corpora. We present an exploration of active learning approaches applied to three grounded language problems of varying complexity in order to analyze what methods are suitable for improving data efficiency in learning. We present a method for analyzing the complexity of data in this joint problem space, and report on how characteristics of the underlying task, along with design decisions such as feature selection and classification model, drive the results. We observe that representativeness, along with diversity, is crucial in selecting data samples.
University of Oxford（牛津大学）；UC Berkeley（加州大学伯克利分校）；IBM Research, Zurich（IBM研究院苏黎世分部）
We present Acquisition Thompson Sampling (ATS), a novel technique for batch Bayesian Optimization (BO) based on the idea of sampling multiple acquisition functions from a stochastic process. We define this process through the dependency of the acquisition functions on a set of model hyper-parameters. ATS is conceptually simple, straightforward to implement and, unlike other batch BO methods, it can be employed to parallelize any sequential acquisition function or to make existing parallel methods scale further. We present experiments on a variety of benchmark functions and on the hyper-paramet…
Fudan University（复旦大学）；The University of Texas at Dallas（德克萨斯大学达拉斯分校）
Bayesian optimization is a promising methodology for analog circuit synthesis. However, the sequential nature of the Bayesian optimization framework significantly limits its ability to fully utilize real-world computational resources. In this paper, we propose an efficient parallelizable Bayesian optimization algorithm via Multi-objective ACquisition function Ensemble (MACE) to further accelerate the optimization procedure. By sampling query points from the Pareto front of the probability of improvement (PI), expected improvement (EI) and lower confidence bound (LCB), we combine the benefits o…
Technische Universität München（慕尼黑工业大学）；KUKA Roboter GmbH（库卡机器人有限公司）；Johns Hopkins University（约翰斯·霍普金斯大学）
Robotic ultrasound has the potential to assist and guide physicians during interventions. In this work, we present a set of methods and a workflow to enable autonomous MRI-guided ultrasound acquisitions. Our approach uses a structured-light 3D scanner for patient-to-robot and image-to-patient calibration, which in turn is used to plan 3D ultrasound trajectories. These MRI-based trajectories are followed autonomously by the robot and are further refined online using automatic MRI/US registration. Despite the low spatial resolution of structured light scanners, the initial planned acquisition pa…
Runtime optimization of acquisition trajectories for X-ray computed tomography with a robotic sample holder
Technical University of Munich（慕尼黑工业大学）；Munich Institute of Biomedical Engineering（慕尼黑生物医学工程研究所）；Klinikum rechts der Isar（伊萨尔河右岸医院）；Institute for Advanced Study（高等研究院）
Implicit Neural Representations for Breathing-compensated Volume Reconstruction in Robotic Ultrasound
Technical University of Munich（慕尼黑工业大学）；Sharif University of Technology（谢里夫理工大学）；Stanford University School of Medicine（斯坦福大学医学院）；ImFusion GmbH（ImFusion有限公司）；Munich Center for Machine Learning（慕尼黑机器学习中心）
OpenRC: An Open-Source Robotic Colonoscopy Framework for Multimodal Data Acquisition and Autonomy Research
Walker Department of Mechanical Engineering, The University of Texas at Austin（德克萨斯大学奥斯汀分校沃克机械工程系）；Department of Surgical Oncology, Division of Surgery, The University of Texas MD Anderson Cancer Center（德克萨斯大学MD安德森癌症中心外科肿瘤学系）；School of Medicine and Health, Technical University of Munich（慕尼黑工业大学医学与健康学院）
RAPTAR: Radar Radiation Pattern Acquisition through Automated Collaborative Robotics
Department of Mechanical and Mechatronics Engineering, University of Waterloo（滑铁卢大学机械与机电工程系）；Department of Electrical and Computer Engineering, University of Waterloo（滑铁卢大学电气与计算机工程系）
Shape Completion and Real-Time Visualization in Robotic Ultrasound Spine Acquisitions
Computer Aided Medical Procedures, Technical University of Munich（技术大学慕尼黑计算机辅助医疗程序）；Konrad Zuse School of Excellence in Reliable AI (relAI)（Konrad Zuse卓越人工智能学校）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）
Tactile Active Inference Reinforcement Learning for Efficient Robotic Manipulation Skill Acquisition
Research Center for Intelligent Robotics, School of Astronautics, Northwestern Polytechnical University（西北工业大学航天学院智能机器人研究中心）；National Key Laboratory of Aerospace Flight Dynamics, Northwestern Polytechnical University（西北工业大学航空航天飞行动力学全国重点实验室）
An important and emerging component of planetary exploration is sample retrieval and return to Earth. Obtaining and analyzing rock samples can provide unprecedented insight into the geology, geo-history and prospects for finding past life and water. Current methods of exploration rely on mission scientists to identify objects of interests and this presents major operational challenges. Finding objects of interests will require systematic and efficient methods to quickly and correctly evaluate the importance of hundreds if not thousands of samples so that the most interesting are saved for furt…
University of Birmingham（伯明翰大学）；The University of Auckland（奥克兰大学）
Algorithms based on deep network models are being used for many pattern recognition and decision-making tasks in robotics and AI. Training these models requires a large labeled dataset and considerable computational resources, which are not readily available in many domains. Also, it is difficult to explore the internal representations and reasoning mechanisms of these models. As a step towards addressing the underlying knowledge representation, reasoning, and learning challenges, the architecture described in this paper draws inspiration from research in cognitive systems. As a motivating exa…
The Chinese University of Hong Kong（香港中文大学）；Shun Hing Institute of Advanced Engineering, The Chinese University of Hong Kong（香港中文大学信兴高等工程研究所）；National University of Singapore（新加坡国立大学）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；Southern University of Science and Technology（南方科技大学）；Shenzhen Research Institute of the Chinese University of Hong Kong（香港中文大学深圳研究院）；Singapore General Hospital（新加坡中央医院）
A novel palpation-based incision detection strategy in the laryngeal region, potentially for robotic tracheotomy, is proposed in this letter. A tactile sensor is introduced to measure tissue hardness in the specific laryngeal region by gentle contact. The kernel fusion method is proposed to combine the Squared Exponential (SE) kernel with Ornstein-Uhlenbeck (OU) kernel to figure out the drawbacks that the existing kernel functions are not sufficiently optimal in this scenario. Moreover, we further regularize exploration factor and greed factor, and the tactile sensor's moving distance and the …
Closed-Loop Magnetic Manipulation for Robotic Transesophageal Echocardiography
The Chinese University of Hong Kong（香港中文大学）；Yuanhua Robotics, Perception & AI Technologies Ltd（源华机器人感知与人工智能技术有限公司）；Southern University of Science and Technology（南方科技大学）；Shenzhen Research Institute of the Chinese University of Hong Kong（香港中文大学深圳研究院）
Anatomical Prior-Driven Framework for Autonomous Robotic Cardiac Ultrasound Standard View Acquisition
State Key Laboratory of Intelligent Manufacturing Equipment and Technology, Huazhong University of Science and Technology（华中科技大学智能制造装备与技术国家重点实验室）；School of Biomedical Engineering, Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳）生物医学工程学院）；Information Intelligence Lab, Department of Electrical Engineering, National Chung Hsing University（中原大学电子工程系信息智能实验室）；Institute of Medical Equipment Science and Engineering, Huazhong University of Science and Technology（华中科技大学医学装备科学与工程研究院）；Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology（华中科技大学同济医学院附属同济医院超声医学科）；Institute of Systems Science (ISS), National University of Singapore (NUS)（新加坡国立大学系统科学研究所）
Analysis of sediments from rivers, lakes, reservoirs, wetlands and other constructed surface water impoundments is an important tool to characterize the function and health of these systems, but is generally carried out manually. This is costly and can be hazardous and difficult for humans due to inaccessibility, contamination, or availability of required equipment. Robotic sampling systems can ease these burdens, but little work has examined the efficiency of such sampling means and no prior work has investigated the quality of the resulting samples. This paper presents an experimental study …
RobotSmith: Generative Robotic Tool Design for Acquisition of Complex Manipulation Skills
University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；Massachusetts Institute of Technology（麻省理工学院）；National University of Singapore（新加坡国立大学）；NVIDIA（英伟达）；MIT-IBM Watson AI Lab（麻省理工-IBM沃森人工智能实验室）
Indian Institute of Technology Kharagpur（印度理工学院哈拉格普尔分校）
Sampling based methods are widely used for robotic motion planning. Traditionally, these samples are drawn from probabilistic ( or deterministic ) distributions to cover the state space uniformly. Despite being probabilistically complete, they fail to find a feasible path in a reasonable amount of time in constrained environments where it is essential to go through narrow passages (bottleneck regions). Current state of the art techniques train a learning model (learner) to predict samples selectively on these bottleneck regions. However, these algorithms depend completely on samples generated …
College of Engineering, University of Michigan（密歇根大学工程学院）；Centre for Autonomous Systems (CAS), University of Technology Sydney（悉尼科技大学自主系统中心）
In this article, we propose a sampling-based motion planning algorithm equipped with an information-theoretic convergence criterion for incremental informative motion planning. The proposed approach allows dense map representations and incorporates the full state uncertainty into the planning process. The problem is formulated as a constrained maximization problem. Our approach is built on rapidly-exploring information gathering algorithms and benefits from advantages of sampling-based optimal motion planning algorithms. We propose two information functions and their variants for fast and onli…
Sampling-based motion planning techniques have emerged as an efficient algorithmic paradigm for solving complex motion planning problems. These approaches use a set of probing samples to construct an implicit graph representation of the robot's state space, allowing arbitrarily accurate representations as the number of samples increases to infinity. In practice, however, solution trajectories only rely on a few critical states, often defined by structure in the state space (e.g., doorways). In this work we propose a general method to identify these critical states via graph-theoretic technique…
University of Stuttgart（斯图加特大学）；TU Berlin（柏林工业大学）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）
Efficient sampling from constraint manifolds, and thereby generating a diverse set of solutions for feasibility problems, is a fundamental challenge. We consider the case where a problem is factored, that is, the underlying nonlinear program is decomposed into differentiable equality and inequality constraints, each of which depends only on some variables. Such problems are at the core of efficient and robust sequential robot manipulation planning. Naive sequential conditional sampling of individual variables, as well as fully joint sampling of all variables at once (e.g., leveraging optimizat…
Drift is a Sampling Error: SNR-Aware Power Distributions for Long-Horizon Robotic Planning
Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences（中国科学院重庆绿色智能技术研究院）；Chongqing School, University of Chinese Academy of Sciences（中国科学院大学重庆学院）
Reinforcement learning (RL) algorithms hold the promise of enabling autonomous skill acquisition for robotic systems. However, in practice, real-world robotic RL typically requires time consuming data collection and frequent human intervention to reset the environment. Moreover, robotic policies learned with RL often fail when deployed beyond the carefully controlled setting in which they were learned. In this work, we study how these challenges can all be tackled by effective utilization of diverse offline datasets collected from previously seen tasks. When faced with a new task, our system a…
Google Brain（谷歌大脑）；University of New Mexico（新墨西哥大学）
We present PRM-RL, a hierarchical method for long-range navigation task completion that combines sampling based path planning with reinforcement learning (RL). The RL agents learn short-range, point-to-point navigation policies that capture robot dynamics and task constraints without knowledge of the large-scale topology. Next, the sampling-based planners provide roadmaps which connect robot configurations that can be successfully navigated by the RL agent. The same RL agents are used to control the robot under the direction of the planning, enabling long-range navigation. We use the Probabili…
Technical University of Munich（慕尼黑工业大学）；Shanghai Jiao Tong University（上海交通大学）；Johns Hopkins University（约翰斯·霍普金斯大学）
Robotic ultrasound (US) imaging aims at overcoming some of the limitations of free-hand US examinations, e.g. difficulty in guaranteeing intra- and inter-operator repeatability. However, due to anatomical and physiological variations between patients and relative movement of anatomical substructures, it is challenging to robustly generate optimal trajectories to examine the anatomies of interest, in particular, when they comprise articulated joints. To address this challenge, this paper proposes a vision-based approach allowing autonomous robotic US limb scanning. To this end, an atlas MRI tem…
Hamburg University of Technology（汉堡工业大学）；University Medical Center Hamburg-Eppendorf（汉堡-埃彭多夫大学医学中心）
In pathology and legal medicine, the histopathological and microbiological analysis of tissue samples from infected deceased is a valuable information for developing treatment strategies during a pandemic such as COVID-19. However, a conventional autopsy carries the risk of disease transmission and may be rejected by relatives. We propose minimally invasive biopsy with robot assistance under CT guidance to minimize the risk of disease transmission during tissue sampling and to improve accuracy. A flexible robotic system for biopsy sampling is presented, which is applied to human corpses placed…
Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation
Technische Universität Berlin（柏林工业大学）；Robotics Institute Germany（德国机器人研究所）；LAAS-CNRS（法国国家科学研究中心系统分析与架构实验室）；Inria & DI-ENS, PSL Research University（法国国家信息与自动化研究所 & 巴黎文理研究大学计算机科学系）
We present a unified framework which supports grounding natural-language semantics in robotic driving. This framework supports acquisition (learning grounded meanings of nouns and prepositions from human annotation of robotic driving paths), generation (using such acquired meanings to generate sentential description of new robotic driving paths), and comprehension (using such acquired meanings to support automated driving to accomplish navigational goals specified in natural language). We evaluate the performance of these three tasks by having independent human judges rate the semantic fidelit…
The potential of large tactile arrays to improve robot perception for safe operation in human-dominated environments and of high-resolution tactile arrays to enable human-level dexterous manipulation is well accepted. However, the increase in the number of tactile sensing elements introduces challenges including wiring complexity, data acquisition, and data processing. To help address these challenges, we develop a tactile sensing technique based on compressed sensing. Compressed sensing simultaneously performs data sampling and compression with recovery guarantees and has been successfully ap…
K2MUSE: A human lower-limb multimodal walking dataset spanning task and acquisition variability for rehabilitation robotics
State Key Laboratory of Robotics and Intelligent Systems, Shenyang Institute of Automation, Chinese Academy of Sciences（机器人与智能系统国家重点实验室，沈阳自动化研究所，中国科学院）；University of Chinese Academy of Sciences（中国科学院大学）；College of Artificial Intelligence, Tianjin Key Laboratory of Intelligent Robotics, Nankai University（人工智能学院，天津智能机器人重点实验室，南开大学）；School of Artificial Intelligence and Automation, Huazhong University of Science and Technology（人工智能与自动化学院，华中科技大学）
Northern Arizona University（北亚利桑那大学）；Federation University Australia（澳大利亚联邦大学）
This paper discusses the adaptive sampling problem in a nonholonomic mobile robotic sensor network for efficiently monitoring a spatial field. It is proposed to employ Gaussian process to model a spatial phenomenon and predict it at unmeasured positions, which enables the sampling optimization problem to be formulated by the use of the log determinant of a predicted covariance matrix at next sampling locations. The control, movement and nonholonomic dynamics constraints of the mobile sensors are also considered in the adaptive sampling optimization problem. In order to tackle the nonlinearity …
Motion Planning for Robotics: A Review for Sampling-based Planners
Chair of Robotics, Artificial Intelligence and Real-time Systems, TUM School of Computation, Information and Technology (CIT), Technical University of Munich（机器人、人工智能与实时系统教授会，技术大学慕尼黑计算、信息与技术学院）；Human-Robot Interfaces and Interaction(HRI2), Istituto Italiano Di Tecnologia (IIT)（人机接口与交互（HRI2），意大利技术研究所）；Chair of Robotics and Systems Intelligence, Munich Institute of Robotics and Machine Intelligence (MIRMI), Technical University of Munich（机器人与系统智能教授会，慕尼黑机器人与机器智能研究所，技术大学慕尼黑）；School of Control Science and Engineering, Shandong University（控制科学与工程学院，山东大学）；School of Computer Science, University of Nottingham（计算机科学学院，诺丁汉大学）
Visuotactile and Explicitly Force-Controlled Robotic Ultrasound for Abdominal Volumetric Reconstruction
Stanford Robotics Laboratory, Computer Science Department, Stanford University（斯坦福机器人实验室、计算机科学系、斯坦福大学）；Department of Radiology, School of Medicine, Stanford University（放射科、医学院、斯坦福大学）
E$^2$DT: Efficient and Effective Decision Transformer with Experience-Aware Sampling for Robotic Manipulation
School of Computer Science, Wuhan University（武汉大学计算机学院）；State Key Lab of Internet of Things for Smart City and Department of Computer Information Science, University of Macau（澳门大学物联网智能城市重点实验室和计算机信息科学系）
National University of Singapore（新加坡国立大学）；Carnegie Mellon University（卡内基梅隆大学）
Recent research in multi-robot exploration and mapping has focused on sampling environmental fields, which are typically modeled using the Gaussian process (GP). Existing information-theoretic exploration strategies for learning GP-based environmental field maps adopt the non-Markovian problem structure and consequently scale poorly with the length of history of observations. Hence, it becomes computationally impractical to use these strategies for in situ, real-time active sampling. To ease this computational burden, this paper presents a Markov-based approach to efficient information-theoret…
Robotic Multimodal Data Acquisition for In-Field Deep Learning Estimation of Cover Crop Biomass
Department of Soil and Crop Sciences, Texas A&M University（土壤与作物科学系，德克萨斯A&M大学）；Department of Computer Science and Engineering, Texas A&M University（计算机科学与工程系，德克萨斯A&M大学）；Department of Mechanical Engineering, Texas A&M University（机械工程系，德克萨斯A&M大学）；College of Agriculture, Food and Natural Resources, Prairie View A&M University（农业、食品与自然资源学院，普里奥里视A&M大学）
TGM-VLA: Task-Guided Mixup for Sampling-Efficient and Robust Robotic Manipulation
Shenzhen International Graduate School, Tsinghua University, Shenzhen, China（清华大学深圳国际研究生院）；The National and Local Co-Build Humanoid Robotics Innovation Center（国家级与地方共建人形机器人创新中心）
