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IEEE International Conference on robotics and Automation (ICRA)

作者： Leonardo Barcellona Alberto Bacchin Alberto Gottardi Emanuele Menegatti Stefano Ghidoni Department of Information Engineering Intelligent Autonomous System Lab University of Padova Padua Italy Politecnico di Torino Torino Italy IT +Robotics srl Vicenza Italy

Robot grasping has been widely studied in the last decade. Recently, Deep Learning made possible to achieve remarkable results in grasp pose estimation, using depth and RGB images. However, only few works consider the choice of the object to grasp. Moreover, they require a huge amount of data for generalizing to unseen object categories. For this reason, we introduce the Few-shot Semantic Grasping task where the objective is inferring a correct grasp given only five labelled images of a target unseen object. We propose a new deep learning architecture able to solve the aforementioned problem, leveraging on a Few-shot Semantic Segmentation module. We have evaluated the proposed model both in the Graspnet dataset and in a real scenario. In Graspnet, we achieve 40,95% accuracy in the Few-shot Semantic Grasping task, outperforming baseline approaches. In the real experiments, the results confirmed the generalization ability of the network.

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Convergence Analysis for Delay Dependent LFC Scheme for Power Systems Networks Over Open Communication Networks with Disturbances

Convergence Analysis for Delay Dependent LFC Scheme for Powe...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Shafiqul Islam Dual-Degree Computer Science and Engineering Intelligent Robotics Mechatronics and Autonomous Systems Lab NCF 263 Xavier University of Louisiana New Orleans LA USA

This paper presents a delay-dependent convergence criterion for load frequency control (LFC) for smart power grid networks with the presence of disturbances and time-varying delays. The LFC scheme comprises delayed remote measurement and control signals with local state feedback signals. The convergence criteria are developed by using Lyapunov-Krasovskii functional for both symmetrical and asymmetrical time-varying delays associated with remote measurement and control channel. First, the analysis shows that all the power system states are bounded and asymptotically stable without the presence of disturbances. Then, the convergence analysis proves that all the power systems states are bounded and their bounds guarantee the convergence property of the $H_{\infty}$ control with the presence of bounded disturbances. These convergence properties are achieved by using well know Linear Matrix Inequalities (LMIs). Compared with the existing design, the proposed analysis uses both symmetrical and asymmetrical time-varying delays in the measurement channel from RTU to the control center and in the control channel from the control center to the power generation unit. In contrast with reported designs, the proposed design and analysis uses the disturbances associated with the closed loop systems appearing from modeling errors and other external load disturbances and faults.

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ROS-health: An open-source framework for neurorobotics

ROS-health: An open-source framework for neurorobotics

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2018 IEEE International Conference on Simulation, Modeling, and Programming for autonomous Robots, SIMPAR 2018

作者： Beraldo, Glori Castaman, Nicola Bortoletto, Roberto Pagello, Enrico Del, Millan Jose R. Tonin, Luca Menegatti, Emanuele Intelligent Autonomous System Lab Department of Information Engineering University of Padova Padua Italy Italy IT+ROBOTICS SRL Contrá Valmerlara 21 Vicenza Italy Defitech Depart in Brain-Machine Interface École Polytechnique Fédérale de Lausanne Chemin des Mines 9 GenevaCH-1202 Switzerland

ISBN: (纸本)9781538659748

This paper aims at providing a preliminary description of ROS-Health, a novel framework for neurorobotics based on the middleware Robot Operating System (ROS). The increased interest in the neurorobotics field and the proliferation of several (neuro)physiological-based applications to control robotics devices made clear the importance to establish a standardized research platform in order to facilitate the distribution of the software, the replication of experimental results and the creation of an unified community to share and manage the code in the years. For this reason, we propose a common platform developed in the ROS ecosystem that takes advantage of its tools and capabilities. Furthermore, we describe the design guidelines that we are following in the preliminary definition of ROS-Health architecture. Finally, we present two illustrative use cases that highlight the advantages and benefits of the adoption of ROS-Health. © 2018 IEEE.

关键词： Robot Operating System

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autonomous marine sampling enhanced by strategically deployed drifters in marine flow fields

arXiv

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arXiv 2018年

作者： Hansen, Johanna Manjanna, Sandeep Li, Alberto Quattrini Rekleitis, Ioannis Dudek, Gregory Mobile Robotics Lab Centre for Intelligent Machines McGill University MontrealQC Canada Autonomous Field Robotics Lab University of South Carolina ColumbiaSC United States Department of Computer Science Dartmouth College HanoverNH United States

We present a transportable system for ocean observations in which a small autonomous surface vehicle (ASV) adaptively collects spatially diverse samples with aid from a team of inexpensive, passive floating sensors known as drifters. Drifters can provide an increase in spatial coverage at little cost as they are propelled about the survey area by the ambient flow field instead of with actuators. Our iterative planning approach demonstrates how we can use the ASV to strategically deploy drifters into points of the flow field for high expected information gain, while also adaptively sampling the space. In this paper, we examine the performance of this heterogeneous sensing system in simulated flow field experiments. Copyright © 2018, The Authors. All rights reserved.

关键词： Flow fields

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Low-cost Scalable People Tracking System for Human-Robot Collaboration in Industrial Environment

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Procedia Manufacturing 2020年 51卷 116-124页

作者： Matteo Terreran Edoardo Lamon Stefano Michieletto Enrico Pagello Intelligent Autonomous Systems Laboratory (IAS-Lab) Dept. of Information Engineering (DEI) University of Padova Padova Italy Robotics and Automation Dept. of Information Engineering Universitä degli Studi di Pisa Pisa Italy

Human-robot collaboration is one of the key elements in the Industry 4.0 revolution, aiming to a close and direct collaboration between robots and human workers to reach higher productivity and improved ergonomics. The first step toward such kind of collaboration in the industrial context is the removal of physical safety barriers usually surrounding standard robotic cells, so that human workers can approach and directly collaborate with robots. Anyway, human safety must be granted avoiding possible collisions with the robot. In this work, we propose the use of a people tracking algorithm to monitor people moving around a robot manipulator and recognize when a person is too close to the robot while performing a task. The system is implemented by a camera network system positioned around the robot workspace, and thoroughly evaluated in different industry-like settings in terms of both tracking accuracy and detection delay.

关键词： People tracking human-robot collaboration low-cost industrial

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Combining Top-down Spatial Reasoning and Bottom-up Object Class Recognition for Scene Understanding

Combining Top-down Spatial Reasoning and Bottom-up Object Cl...

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IEEE/RSJ International Conference on intelligent Robots and Systems

作者： Lars Kunze Chris Burbridge Marina Alberti Akshaya Tippur John Folkesson Patric Jensfelt Nick Hawes Intelligent Robotics Lab School of Computer Science University of Birmingham United Kingdom Centre for Autonomous Systems KTH Royal Institute of Technology SE-100 44 Stockholm Sweden

ISBN: (纸本)9781479969357

Many robot perception systems are built to only consider intrinsic object features to recognise the class of an object. By integrating both top-down spatial relational reasoning and bottom-up object class recognition the overall performance of a perception system can be improved. In this paper we present a unified framework that combines a 3D object class recognition system with learned, spatial models of object relations. In robot experiments we show that our combined approach improves the classification results on real world office desks compared to pure bottom-up perception. Hence, by using spatial knowledge during object class recognition perception becomes more efficient and robust and robots can understand scenes more effectively.

关键词： object class Object Relations Robot bottom-up spatial reasoning senses Perception top-down

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Learning to Model Diverse Driving Behaviors in Highly Interactive autonomous Driving Scenarios with Multi-Agent Reinforcement Learning

arXiv

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arXiv 2024年

作者： Weiwei, Liu Wenxuan, Hu Wei, Jing Lanxin, Lei Lingping, Gao Yong, Liu The Advanced Perception on Robotics and Intelligent Learning Lab College of Control Science and Enginneering Zhejiang University Hangzhou310027 China The Advanced Perception on Robotics and Intelligent Learning Lab Huzhou Institute Zhejiang University Huzhou China College of Information Engineering Huzhou University Huzhou China Department of Autonomous Driving Lab Alibaba DAMO Academy Hangzhou China

autonomous vehicles trained through Multi-Agent Reinforcement Learning (MARL) have shown impressive results in many driving scenarios. However, the performance of these trained policies can be impacted when faced with diverse driving styles and personalities, particularly in highly interactive situations. This is because conventional MARL algorithms usually operate under the assumption of fully cooperative behavior among all agents and focus on maximizing team rewards during training. To address this issue, we introduce the Personality Modeling Network (PeMN), which includes a cooperation value function and personality parameters to model the varied interactions in high-interactive scenarios. The PeMN also enables the training of a background traffic flow with diverse behaviors, thereby improving the performance and generalization of the ego vehicle. Our extensive experimental studies, which incorporate different personality parameters in high-interactive driving scenarios, demonstrate that the personality parameters effectively model diverse driving styles and that policies trained with PeMN demonstrate better generalization compared to traditional MARL methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Towards robust online inverse dynamics learning

Towards robust online inverse dynamics learning

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IEEE/RSJ International Conference on intelligent Robots and Systems

作者： Franziska Meier Daniel Kappler Nathan Ratliff Stefan Schaal CLMC Lab University of Southern California Los Angeles USA Autonomous Motion Department MPI for Intelligent Systems Tubingen Germany Lula Robotics Inc. United States

ISBN: (纸本)9781509037636

Learning of inverse dynamics modeling errors is key for compliant or force control when analytical models are only rough approximations. Thus, designing real time capable function approximation algorithms has been a necessary focus towards the goal of online model learning. However, because these approaches learn a mapping from actual state and acceleration to torque, good tracking is required to observe data points on the desired path. Recently it has been shown how online gradient descent on a simple modeling error offset term to minimize tracking at acceleration level can address this issue. However, to adapt to larger errors a high learning rate of the online learner is required, resulting in reduced compliancy. Thus, here we propose to combine both approaches: The online adapted offset term ensures good tracking such that a nonlinear function approximator is able to learn an error model on the desired trajectory. This, in turn, reduces the load on the adaptive feedback, enabling it to use a lower learning rate. Combined this creates a controller with variable feedback and low gains, and a feedforward model that can account for larger modeling errors. We demonstrate the effectiveness of this framework, in simulation and on a real system.

关键词： inverse kinematics error model on-line model Force control learning rate acceleration ANALYSIS MODELS tracking Learning Controllers

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Semantic Object Maps for Robotic Housework - Representation, Acquisition and Use

Semantic Object Maps for Robotic Housework - Representation,...

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IEEE/RSJ International Conference on intelligent robotics and Systems

作者： Dejan Pangercic Benjamin Pitzer Moritz Tenorth Michael Beetz Robert Bosch LLC Palo Alto CA Intelligent Robotics and Communications Lab ATR Kyoto Japan Intelligent Autonomous Systems/Artificial Intelligence Department of Computer Science and Centre for Computing Technologies (TZI) University of Bremen Germany

ISBN: (纸本)9781467317375

In this article we investigate the representation and acquisition of Semantic Objects Maps (SOMs) that can serve as information resources for autonomous service robots performing everyday manipulation tasks in kitchen environments. These maps provide the robot with information about its operation environment that enable it to perform fetch and place tasks more efficiently and reliably. To this end, the semantic object maps can answer queries such as the following ones: "What do parts of the kitchen look like?", "How can a container be opened and closed?", "Where do objects of daily use belong?", "What is inside of cupboards/drawers?", etc. The semantic object maps presented in this article, which we call SOM~+, extend the first generation of SOMs presented by Rusu et al. [1] in that the representation of SOM~+ is designed more thoroughly and that SOM~+ also include knowledge about the appearance and articulation of furniture objects. Also, the acquisition methods for SOM~+ substantially advance those developed in [1] in that SOM~+ are acquired autonomously and with low-cost (Kinect) instead of very accurate (laser-based) 3D sensors. In addition, perception methods are more general and are demonstrated to work in different kitchen environments.

关键词： general environments furniture Furnishings GRHL3 gene self-organising feature maps Environments KITCHEN Semantics maps Service robots Household Management Information Resources fetch Joints

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Self-learning visual inspection system for cable crimping machines

Self-learning visual inspection system for cable crimping ma...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Stefano Ghidoni Matteo Finotto Emanuele Menegatti Intelligent Autonomous Systems Laboratory (IAS-Lab) The University of Padova Padova Italy IT+Robotics s.r.l. A University of Padova Spin-off Company Padova Italy

This paper presents a system for checking connectors while cables are being crimped to them. The system verifies that the wires color sequence is correct: an accurate color analysis technique has then been developed, ...

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