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Unfolding the Literature: A Review of Robotic Cloth Manipulation

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arXiv 2024年

作者： Longhini, Alberta Wang, Yufei Garcia-Camacho, Irene Blanco-Mulero, David Moletta, Marco Welle, Michael Alenyà, Guillem Yin, Hang Erickson, Zackory Held, David Borràs, Júlia Kragic, Danica Department of Robotics Perception and Learning KTH Royal Institute of Technology Stockholm100 44 Sweden The Robotics Institute Carnegie Mellon University PittsburghPA15213 United States Institut de Robòtica i Informàtica Industrial CSIC-UPC Barcelona08028 Spain Department of Computer Science University of Copenhagen Copenhagen2100 Denmark

The realm of textiles spans clothing, households, healthcare, sports, and industrial applications. The deformable nature of these objects poses unique challenges that prior work on rigid objects cannot fully address. The increasing interest within the community in textile perception and manipulation has led to new methods that aim to address challenges in modeling, perception, and control, resulting in significant progress. However, this progress is often tailored to one specific textile or a subcategory of these textiles. To understand what restricts these methods and hinders current approaches from generalizing to a broader range of real-world textiles, this review provides an overview of the field, focusing specifically on how and to what extent textile variations are addressed in modeling, perception, benchmarking, and manipulation of textiles. We finally conclude by identifying key open problems and outlining grand challenges that will drive future advancements in the field. Copyright © 2024, The Authors. All rights reserved.

关键词： Textiles

HRI Wasn’t Built In a Day: A Call To Action For Responsible HRI Research

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HRI Wasn’t Built In a Day: A Call To Action For Responsible...

IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Micol Spitale Rebecca Stower Maria Teresa Parreira Elmira Yadollahi Iolanda Leite Hatice Gunes Department of Computer Science and Technology University of Cambridge UK Department of Electronic Information and Bio-Engineering Politecnico di Milano Italy Division of Robotics Perception and Learning KTH Royal Institute of Technology Information Science Cornell University School of Computing and Communications Lancaster University UK

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

In recent years, the awareness of the academy around responsible research has notably increased. For instance, with advances in machine learning and artificial intelligence, recent efforts have been made to promote ethical, fair, and inclusive AI and robotics. To better understand if and to what extent HRI is incentivizing researchers to engage in responsible research, we conducted an exploratory review of the publishing guidelines for the most popular HRI conference venues. We identified 18 conferences which published at least 7 HRI papers in 2022. From these, we discuss four themes relevant to conducting responsible HRI research in line with the Responsible Research and Innovation framework: ethical and human participant considerations, transparency and reproducibility, accessibility and inclusion, and plagiarism and LLM use. We identify several gaps and room for improvement within HRI regarding responsible research. Finally, we establish a call to action to provoke conversations among HRI researchers about the importance of conducting responsible research within emerging fields like HRI.

关键词： Ethics Technological innovation Reviews Rail to rail inputs Human-robot interaction Oral communication Reproducibility of results Stakeholders Robots Guidelines

ASFM: Augmented Social Force Model for Legged Robot Social Navigation

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ASFM: Augmented Social Force Model for Legged Robot Social N...

IEEE-RAS International Conference on Humanoid Robots

作者： Sebastian Ægidius Rodrigo Chacón-Quesada Andromachi Maria Delfaki Dimitrios Kanoulas Yiannis Demiris Department of Computer Science Robot Perception and Learning Lab University College London Gower Street London UK Department of Electrical and Electronic Engineering Personal Robotics Lab Imperial College London London UK Department of Computer Science AI Centre UCL and the Archimedes/Athena RC Greece

ISBN: (数字)9798350373578

ISBN: (纸本)9798350373585

Social navigation in robotics primarily involves guiding mobile robots through human-populated areas, with pedestrian comfort balanced with efficient path-finding. Although progress has been seen in this field, a solution for the seamless integration of robots into pedestrian settings remains elusive. In this paper, a social force model for legged robots is developed, utilizing visual perception for human localization. In particular, an augmented social force model is introduced, incorporating refined interpretations of repulsive forces and avoidance behaviors based on pedestrian actions, alongside a target following mechanism. Experimental evaluation on a quadruped robot, through various scenarios, including interactions with oncoming pedestrians, crowds, and obstructed paths, demonstrates that the proposed augmented model significantly improves upon previous baseline methods in terms of chosen path length, average velocity, and time-to-goal for effective and efficient social navigation. The code isopen-source, while video demonstrations can be found on the project’s webpage: https://***/ASFM/

关键词： Legged locomotion Location awareness Pedestrians Codes Navigation Force Humanoid robots Quadrupedal robots Visual perception

Benchmarking Classical and learning-Based Multibeam Point Cloud Registration

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Benchmarking Classical and Learning-Based Multibeam Point Cl...

IEEE International Conference on robotics and Automation (ICRA)

作者： Li Ling Jun Zhang Nils Bore John Folkesson Anna Wåhlin Division of Robotics Perception and Learning (RPL) KTH Royal Institute of Technology Stockholm Sweden Institute of Computer Graphics and Vision (ICGV) TU Graz Austria Ocean Infinity Västra Frölunda Sweden Department of Marine Sciences University of Gothenburg Sweden

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Deep learning has shown promising results for multiple 3D point cloud registration datasets. However, in the underwater domain, most registration of multibeam echo-sounder (MBES) point cloud data are still performed using classical methods in the iterative closest point (ICP) family. In this work, we curate and release DotsonEast Dataset, a semi-synthetic MBES registration dataset constructed from an autonomous underwater vehicle in West Antarctica. Using this dataset, we systematically benchmark the performance of 2 classical and 4 learning-based methods. The experimental results show that the learning-based methods work well for coarse alignment, and are better at recovering rough transforms consistently at high overlap (20-50%). In comparison, GICP (a variant of ICP) performs well for fine alignment and is better across all metrics at extremely low overlap (10%). To the best of our knowledge, this is the first work to benchmark both learning-based and classical registration methods on an AUV-based MBES dataset. To facilitate future research, both the code and data are made available online. 1

关键词： Point cloud compression learning systems Measurement Three-dimensional displays Simultaneous localization and mapping Iterative closest point algorithm Transforms

Verifying Nonlinear Neural Feedback Systems using Polyhedral Enclosures

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arXiv 2025年

作者： Akinwande, Samuel I. Sidrane, Chelsea Kochenderfer, Mykel J. Barrett, Clark Department of Aeronautical and Astronautical Engineering Stanford University StanfordCA94305 United States Department of Computer Science Stanford University StanfordCA94305 United States Division of Robotics Perception and Learning Intelligent Systems Department School of Electrical Engineering & Computer Science KTH Royal Institute of Technology Stockholm Sweden

As dynamical systems equipped with neural network controllers (neural feedback systems) become increasingly prevalent, it is critical to develop methods to ensure their safe operation. Verifying safety requires extending control theoretic analysis methods to these systems. Although existing techniques can efficiently handle linear neural feedback systems, relatively few scalable methods address the nonlinear case. We propose a novel algorithm for forward reachability analysis of nonlinear neural feedback systems. The approach leverages the structure of the nonlinear transition functions of the systems to compute tight polyhedral enclosures (i.e., abstractions). These enclosures, combined with the neural controller, are then encoded as a mixed-integer linear program (MILP). Optimizing this MILP yields a sound over-approximation of the forward-reachable set. We evaluate our algorithm on representative benchmarks and demonstrate an order of magnitude improvement over the current state of the art. Copyright © 2025, The Authors. All rights reserved.

关键词： Mixed-integer linear programming

FusionPortableV2: A Unified Multi-Sensor Dataset for Generalized SLAM Across Diverse Platforms and Scalable Environments

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arXiv 2024年

作者： Wei, Hexiang Jiao, Jianhao Hu, Xiangcheng Yu, Jingwen Xie, Xupeng Wu, Jin Zhu, Yilong Liu, Yuxuan Wang, Lujia Liu, Ming Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong Robot Perception and Learning Lab Department of Computer Science University College London United Kingdom Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology China Guangzhou China

Simultaneous Localization and Mapping (SLAM) technology has been widely applied in various robotic scenarios, from rescue operations to autonomous driving. However, the generalization of SLAM algorithms remains a significant challenge, as current datasets often lack scalability in terms of platforms and environments. To address this limitation, we present FusionPortableV2, a multi-sensor SLAM dataset featuring sensor diversity, varied motion patterns, and a wide range of environmental scenarios. Our dataset comprises 27 sequences, spanning over 2.5 hours and collected from four distinct platforms: a handheld suite, a legged robot, a unmanned ground vehicle (UGV), and a vehicle. These sequences cover diverse settings, including buildings, campuses, and urban areas, with a total length of 38.7km. Additionally, the dataset includes ground-truth (GT) trajectories and RGB point cloud maps covering approximately 0.3km2. To validate the utility of our dataset in advancing SLAM research, we assess several state-of-the-art (SOTA) SLAM algorithms. Furthermore, we demonstrate the dataset's broad application beyond traditional SLAM tasks by investigating its potential for monocular depth estimation. The completae dataset, including sensor data, GT, and calibration details, is accessible at https://***/dataset/fusionportable v2. Copyright © 2024, The Authors. All rights reserved.

关键词： SLAM robotics

Non-Axiomatic Reasoning for an Autonomous Mobile Robot

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Non-Axiomatic Reasoning for an Autonomous Mobile Robot

IEEE International Conference on robotics and Automation (ICRA)

作者： Patrick Hammer Peter Isaev Lei Feng Robert Johansson Jana Tumova Division of Robotics Perception and Learning KTH Royal Institute of Technology Stockholm Sweden Department of Computer and Information Systems Temple University Philadelphia PA USA Unit of Mechatronics and Embedded Control Systems KTH Royal Institute of Technology Stockholm Sweden Department of Psychology Stockholm University Stockholm Sweden

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

We present the integration of a Non-Axiomatic Reasoning System (NARS) with mobile robots for planning and decision making. NARS enables robots to effectively handle uncertainty in real-time with complete sensor and actuator integration, thereby ensuring adaptability to evolving scenarios. We discuss essential parts of the logic, the architecture and working principles of NARS, and the integration of NARS as a ROS node. A case study is provided demonstrating the system’s proficiency to carry out a garbage collection task in an open-air environment by operating a mobile robot with manipulator arm, and we demonstrate its ability to learn about the place-dependent accumulation of garbage items. Case study also reveals that our approach performs more effectively on the overall task than the Belief-Desire-Intention model we compared with.

关键词： Actuators Uncertainty Object detection Switches Robot sensing systems Manipulators Cognition

Temporally Robust Multi-Agent STL Motion Planning in Continuous Time

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arXiv 2023年

作者： Verhagen, Joris Lindemann, Lars Tumova, Jana The Division of Robotics Perception and Learning School of Electrical Engineering and Computer Science Kth Royal Institute of Technology Stockholm Sweden The Thomas Lord Department of Computer Science University of Southern California Los AngelesCA United States

Signal Temporal Logic (STL) is a formal language over continuous-time signals (such as trajectories of a multiagent system) that allows for the specification of complex spatial and temporal system requirements (such as staying sufficiently close to each other within certain time intervals). To promote robustness in multi-agent motion planning with such complex requirements, we consider motion planning with the goal of maximizing the temporal robustness of their joint STL specification, i.e. maximizing the permissible time shifts of each agent's trajectory while still satisfying the STL specification. Previous methods presented temporally robust motion planning and control in a discrete-time Mixed Integer Linear Programming (MILP) optimization scheme. In contrast, we parameterize the trajectory by continuous Bézier curves, where the curvature and the time-traversal of the trajectory are parameterized individually. We show an algorithm generating continuous-time temporally robust trajectories and prove soundness of our approach. Moreover, we empirically show that our parametrization realizes this with a considerable speed-up compared to state-of-the-art methods based on constant interval time discretization. Copyright © 2023, The Authors. All rights reserved.

关键词： Trajectories

Comparison between Behavior Trees and Finite State Machines

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arXiv 2024年

作者： Iovino, Matteo Förster, Julian Falco, Pietro Chung, Jen Jen Siegwart, Roland Smith, Christian ABB Corporate Research Västerås Sweden Autonomous Systems Lab ETH Zürich Zürich Switzerland Department of Information Engineering University of Padova Italy School of EECS The University of Queensland Australia Division of Robotics Perception and Learning KTH - Royal Institute of Technology Stockholm Sweden

Behavior Trees (BTs) were first conceived in the computer games industry as a tool to model agent behavior, but they received interest also in the robotics community as an alternative policy design to Finite State Machines (FSMs). The advantages of BTs over FSMs had been highlighted in many works, but there is no thorough practical comparison of the two designs. Such a comparison is particularly relevant in the robotic industry, where FSMs have been the state-of-the-art policy representation for robot control for many years. In this work we shed light on this matter by comparing how BTs and FSMs behave when controlling a robot in a mobile manipulation task. The comparison is made in terms of reactivity, modularity, readability, and design. We propose metrics for each of these properties, being aware that while some are tangible and objective, others are more subjective and implementation dependent. The practical comparison is performed in a simulation environment with validation on a real robot. We find that although the robot’s behavior during task solving is independent on the policy representation, maintaining a BT rather than an FSM becomes easier as the task increases in complexity. Copyright © 2024, The Authors. All rights reserved.

关键词： Computer games