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 that evaluates and optimizes sediment sampling patterns applied to a robot sediment sampling system that allows collection of minimally-disturbed sediment cores from natural and man-made water bodies for various sediment types. To meet this need, we developed and tested a robotic sampling platform in the laboratory to test functionality under a range of sediment types and operating conditions. Specifically, we focused on three patterns by which a cylindrical coring device was driven into the sediment (linear, helical, and zig-zag) for three sediment types (coarse sand, medium sand, and silt). The results show that the optimal sampling pattern varies depending on the type of sediment and can be optimized based on the sampling objective. We examined two sampling objectives: maximizing the mass of minimally disturbed sediment and minimizing the power per mass of sample. This study provides valuable data to aid in the selection of optimal sediment coring methods for various applications and builds a solid foundation for future field testing under a range of environmental conditions.
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 …
University of Nevada, Reno（内华达大学里诺分校）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
The use of random sampling in decision-making and control has become popular with the ease of access to graphic processing units that can generate and calculate multiple random trajectories for real-time robotic applications. In contrast to sequential optimization, the sampling-based method can take advantage of parallel computing to maintain constant control loop frequencies. Inspired by its wide applicability in robotic applications, we calculate a sampling complexity result applicable to general nonlinear systems considered in the path integral method, which is a sampling-based method. The …
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…
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…
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 th…
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）
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）
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）
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）
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）
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…
Feedback-MPPI: Fast Sampling-Based MPC via Rollout Differentiation -- Adios low-level controllers
CNRS（法国国家科学研究中心）；Univ Rennes（雷恩大学）；Inria（法国国家信息与自动化研究所）；IRISA（法国信息与随机系统研究所）；University College London（伦敦大学学院）；Istituto Italiano di Tecnologia (IIT)（意大利理工学院）
A Digital Twin for Robotic Post Mortem Tissue Sampling using Virtual Reality
Institute of Medical Technology and Intelligent Systems, Hamburg University of Technology（医学技术与智能系统研究所，汉堡技术大学）；Institute of Legal Medicine, University Medical Center Hamburg-Eppendorf（法医学研究所，汉堡艾本达大学医学中心）；Interdisciplinary Competence Center for Interface Research（界面研究跨学科能力建设中心）；SustAInLivWork Center of Excellence, Kaunas, Lithuania（可持续性与生活工作卓越中心，考纳斯，立陶宛）
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…
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…
TapSampling: Inference-Time Sampling with a Task-Progress-Understanding Verifier for Robotic Manipulation
Harbin Institute of Technology, China（哈尔滨工业大学，中国）；Harbin Institute of Technology (Weihai) Qingdao Research Institute, China（哈尔滨工业大学（威海）青岛研究院，中国）；Iray Technology co., Ltd., Shandong, China（Iray科技有限公司，山东，中国）；Tsinghua University, Beijing, China（清华大学，北京，中国）
Decoding RobKiNet: Insights into Efficient Training of Robotic Kinematics Informed Neural Network
School of Mechanical Engineering, Shanghai Jiao Tong University（上海交通大学机械工程学院）；Intel Labs China（英特尔中国实验室）；Henan Academy of Sciences（河南省科学院）；Intel CCG FIS（英特尔中国区研究院）
The Chinese University of Hong Kong（香港中文大学）；T Stone Robotics Institute（T Stone机器人研究所）；Hong Kong Center for Logistics Robotics（香港物流机器人中心）
The COVID-19 pandemic has imposed serious challenges in multiple perspectives of human life. To diagnose COVID-19, oropharyngeal swab (OP SWAB) sampling is generally applied for viral nucleic acid (VNA) specimen collection. However, manual sampling exposes medical staff to a high risk of infection. Robotic sampling is promising to mitigate this risk to the minimum level, but traditional robot suffers from safety, cost, and control complexity issues for wide-scale deployment. In this work, we present soft robotic technology is promising to achieve robotic OP swab sampling with excellent swab ma…
Fleets of networked autonomous vehicles (AVs) collect terabytes of sensory data, which is often transmitted to central servers (the ''cloud'') for training machine learning (ML) models. Ideally, these fleets should upload all their data, especially from rare operating contexts, in order to train robust ML models. However, this is infeasible due to prohibitive network bandwidth and data labeling costs. Instead, we propose a cooperative data sampling strategy where geo-distributed AVs collaborate to collect a diverse ML training dataset in the cloud. Since the AVs have a shared objective but min…
California State University, Monterey Bay（加州州立大学蒙特雷湾分校）；The Jackson Laboratory（杰克逊实验室）；Scripps Institution of Oceanography（斯克里普斯海洋研究所）；The Ocean Cleanup Foundation（海洋清理基金会）；Hawaii Pacific University（夏威夷太平洋大学）
The quantification of positively buoyant marine plastic debris is critical to understanding how plastic litter accumulates across the world's oceans and is also crucial to identifying hotspots for targeted cleanup efforts. Currently, the most common method to quantify marine plastic is using manta trawls for manual sampling. However, this method is cost-intensive and requires human labor. This study removes the need for manual sampling by using an autonomous method using neural networks and computer vision models, which trained on images captured from various layers of the ocean column to perf…
A3P5 NEMESIS Integrated Rover Design for Environmental Reconnaissance and Robotic Sampling with Reproducible Mobility Analysis and an External Data Machine Learning Calibration Benchmark
St. Joseph Higher Secondary School（圣约瑟夫高等中学）；BAF Shaheen College Kurmitola（孟加拉国空军沙欣学院库尔米托拉分校）；Greenland Residential School（格陵兰寄宿学校）
Adjusting Tissue Puncture Omnidirectionally In Situ with Pneumatic Rotatable Biopsy Mechanism and Hierarchical Airflow Management in Tortuous Luminal Pathways
Department of Electronic Engineering, The Chinese University of Hong Kong（电子工程系，香港中文大学）；Department of Biomedical Engineering, The Chinese University of Hong Kong（生物医学工程系，香港中文大学）；School of Robotics and Advanced Manufacturing, Harbin Institute of Technology（机器人与先进制造学院，哈尔滨工业大学）；School of Medical and Engineering, Harbin Institute of Technology（医学与工程学院，哈尔滨工业大学）
Mining beyond Earth with Space Robots: Exploration, Sampling, and Extraction
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；OpenSpace Lab（OpenSpace实验室）；School of Computation, Information and Technology, Technical University of Munich（慕尼黑工业大学计算、信息与技术学院）；University Research and Innovation Center, Obuda University（欧布达大学大学研究与创新中心）；School of Mechanical Engineering and Automation, Beihang University（北京航空航天大学机械工程及自动化学院）；School of Earth and Space Sciences, Wuhan University（武汉大学地球与空间科学学院）；Faculty of Mathematics and Computer Science, University of Würzburg（维尔茨堡大学数学与计算机科学学院）；School of Architecture & Urban Planning, Shenzhen University（深圳大学建筑与城市规划学院）；School of Mechanical and Electrical Engineering, China University of Mining and（中国矿业大学机电工程学院）
Recent research in robot exploration and mapping has focused on sampling environmental hotspot fields. This exploration task is formalized by Low, Dolan, and Khosla (2008) in a sequential decision-theoretic planning under uncertainty framework called MASP. The time complexity of solving MASP approximately depends on the map resolution, which limits its use in large-scale, high-resolution exploration and mapping. To alleviate this computational difficulty, this paper presents an information-theoretic approach to MASP (iMASP) for efficient adaptive path planning; by reformulating the cost-minimi…
Faculty of Electrical Engineering and Computing, University of Zagreb（萨格勒布大学电气工程与计算学院）
In this paper we propose a robotic system for Irrigation Water Management (IWM) in a structured robotic greenhouse environment. A commercially available robotic manipulator is equipped with an RGB-D camera and a soil moisture sensor. The two are used to automate the procedure known as "feel and appearance method", which is a way of monitoring soil moisture to determine when to irrigate and how much water to apply. We develop a compliant force control framework that enables the robot to insert the soil moisture sensor in the sensitive plant root zone of the soil, without harming the plant. RGB-…
We demonstrate how a sampling-based robotic planner can be augmented to learn to understand a sequence of natural language commands in a continuous configuration space to move and manipulate objects. Our approach combines a deep network structured according to the parse of a complex command that includes objects, verbs, spatial relations, and attributes, with a sampling-based planner, RRT. A recurrent hierarchical deep network controls how the planner explores the environment, determines when a planned path is likely to achieve a goal, and estimates the confidence of each move to trade off exp…
Soil moisture monitoring is a fundamental process to enhance agricultural outcomes and to protect the environment. The traditional methods for measuring moisture content in soil are laborious and expensive, and therefore there is a growing interest in developing sensors and technologies which can reduce the effort and costs. In this work, we propose to use an autonomous mobile robot equipped with a state-of-the-art non-contact soil moisture sensor that builds moisture maps on the fly and automatically selects the most optimal sampling locations. The robot is guided by an autonomous exploration…
Real-time Sampling-based Model Predictive Control based on Reverse Kullback-Leibler Divergence and Its Adaptive Acceleration
Principles of Informatics Research Division（信息学原理研究部）；National Institute of Informatics (NII)（国家信息研究所）；Informatics Program（信息学项目）；The Graduate University for Advanced Studies (SOKENDAI)（高级研究大学（SOKENDAI））；Division of Information Science（信息科学部）
PierGuard: A Planning Framework for Underwater Robotic Inspection of Coastal Piers
Shenzhen Key Laboratory of Robotics Perception and Intelligence（深圳机器人感知与智能重点实验室）；Department of Electronic and Electrical Engineering（电子与电气工程系）；Southern University of Science and Technology（南方科技大学）；Department of Electronic and Computer Engineering（电子与计算机工程系）；Hong Kong University of Science and Technology（香港科学与技术大学）；Jiaxing Research Institute（嘉兴研究院）；Department of Electronic Engineering（电子工程系）；The Chinese University of Hong Kong（香港中文大学）
Robotic grasping traditionally relies on object features or shape information for learning new or applying already learned grasps. We argue however that such a strong reliance on object geometric information renders grasping and grasp learning a difficult task in the event of cluttered environments with high uncertainty where reasonable object models are not available. This being so, in this paper we thus investigate the application of model-free stochastic optimization for grasp learning. For this, our proposed learning method requires just a handful of user-demonstrated grasps and an initial…
Tsinghua University（清华大学）；The University of Hong Kong（香港大学）
Motion planning for robots of high degrees-of-freedom (DOFs) is an important problem in robotics with sampling-based methods in configuration space C as one popular solution. Recently, machine learning methods have been introduced into sampling-based motion planning methods, which train a classifier to distinguish collision free subspace from in-collision subspace in C. In this paper, we propose a novel configuration space decomposition method and show two nice properties resulted from this decomposition. Using these two properties, we build a composite classifier that works compatibly with pr…
Max-Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；Tandon School of Engineering, New York University（纽约大学坦登工程学院）
Model free reinforcement learning suffers from the high sampling complexity inherent to robotic manipulation or locomotion tasks. Most successful approaches typically use random sampling strategies which leads to slow policy convergence. In this paper we present a novel approach for efficient exploration that leverages previously learned tasks. We exploit the fact that the same system is used across many tasks and build a generative model for exploration based on data from previously solved tasks to improve learning new tasks. The approach also enables continuous learning of improved explorati…
