International Symposium of Robotics Research最新文献_第2页

SCIM: Simultaneous Clustering, Inference, and Mapping for Open-World Semantic Scene Understanding 开放世界语义场景理解的同步聚类、推理和映射

International Symposium of Robotics Research Pub Date : 2022-06-21 DOI: 10.48550/arXiv.2206.10670

H. Blum, M. Müller, A. Gawel, R. Siegwart, César Cadena

引用次数: 1

Efficiently Learning Single-Arm Fling Motions to Smooth Garments 有效地学习单臂投掷运动平滑服装

International Symposium of Robotics Research Pub Date : 2022-06-17 DOI: 10.48550/arXiv.2206.08921

Lawrence Yunliang Chen, Huang Huang, Ellen R. Novoseller, Daniel Seita, Jeffrey Ichnowski, Michael Laskey, Richard Cheng, T. Kollar, Ken Goldberg

{"title":"Efficiently Learning Single-Arm Fling Motions to Smooth Garments","authors":"Lawrence Yunliang Chen, Huang Huang, Ellen R. Novoseller, Daniel Seita, Jeffrey Ichnowski, Michael Laskey, Richard Cheng, T. Kollar, Ken Goldberg","doi":"10.48550/arXiv.2206.08921","DOIUrl":"https://doi.org/10.48550/arXiv.2206.08921","url":null,"abstract":"Recent work has shown that 2-arm\"fling\"motions can be effective for garment smoothing. We consider single-arm fling motions. Unlike 2-arm fling motions, which require little robot trajectory parameter tuning, single-arm fling motions are very sensitive to trajectory parameters. We consider a single 6-DOF robot arm that learns fling trajectories to achieve high garment coverage. Given a garment grasp point, the robot explores different parameterized fling trajectories in physical experiments. To improve learning efficiency, we propose a coarse-to-fine learning method that first uses a multi-armed bandit (MAB) framework to efficiently find a candidate fling action, which it then refines via a continuous optimization method. Further, we propose novel training and execution-time stopping criteria based on fling outcome uncertainty; the training-time stopping criterion increases data efficiency while the execution-time stopping criteria leverage repeated fling actions to increase performance. Compared to baselines, the proposed method significantly accelerates learning. Moreover, with prior experience on similar garments collected through self-supervision, the MAB learning time for a new garment is reduced by up to 87%. We evaluate on 36 real garments: towels, T-shirts, long-sleeve shirts, dresses, sweat pants, and jeans. Results suggest that using prior experience, a robot requires under 30 minutes to learn a fling action for a novel garment that achieves 60-94% coverage.","PeriodicalId":136210,"journal":{"name":"International Symposium of Robotics Research","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124033521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

Multi-Object Grasping in the Plane 平面上的多目标抓取

International Symposium of Robotics Research Pub Date : 2022-06-01 DOI: 10.48550/arXiv.2206.00229

Wisdom C. Agboh, Jeffrey Ichnowski, Ken Goldberg, M. Dogar

引用次数: 9

BulletArm: An Open-Source Robotic Manipulation Benchmark and Learning Framework 一个开源的机器人操作基准和学习框架

International Symposium of Robotics Research Pub Date : 2022-05-28 DOI: 10.48550/arXiv.2205.14292

Dian Wang, Colin Kohler, Xu Zhu, Ming Jia, Robert W. Platt

引用次数: 7

Nonlinear Stochastic Trajectory Optimization for Centroidal Momentum Motion Generation of Legged Robots 腿式机器人质心动量运动生成的非线性随机轨迹优化

International Symposium of Robotics Research Pub Date : 2022-05-26 DOI: 10.48550/arXiv.2205.13264

Ahmad Gazar, M. Khadiv, Sébastien Kleff, A. Prete, L. Righetti

{"title":"Nonlinear Stochastic Trajectory Optimization for Centroidal Momentum Motion Generation of Legged Robots","authors":"Ahmad Gazar, M. Khadiv, Sébastien Kleff, A. Prete, L. Righetti","doi":"10.48550/arXiv.2205.13264","DOIUrl":"https://doi.org/10.48550/arXiv.2205.13264","url":null,"abstract":"Generation of robust trajectories for legged robots remains a challenging task due to the underlying nonlinear, hybrid and intrinsically unstable dynamics which needs to be stabilized through limited contact forces. Furthermore, disturbances arising from unmodelled contact interactions with the environment and model mismatches can hinder the quality of the planned trajectories leading to unsafe motions. In this work, we propose to use stochastic trajectory optimization for generating robust centroidal momentum trajectories to account for additive uncertainties on the model dynamics and parametric uncertainties on contact locations. Through an alternation between the robust centroidal and whole-body trajectory optimizations, we generate robust momentum trajectories while being consistent with the whole-body dynamics. We perform an extensive set of simulations subject to different uncertainties on a quadruped robot showing that our stochastic trajectory optimization problem reduces the amount of foot slippage for different gaits while achieving better performance over deterministic planning.","PeriodicalId":136210,"journal":{"name":"International Symposium of Robotics Research","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133964078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Flying Hydraulically Amplified Electrostatic Gripper System for Aerial Object Manipulation 用于空中物体操纵的飞行液压放大静电抓手系统

International Symposium of Robotics Research Pub Date : 2022-05-25 DOI: 10.48550/arXiv.2205.13011

Dario Tscholl, Stephan-Daniel Gravert, Aurel X. Appius, Robert K. Katzschmann

{"title":"Flying Hydraulically Amplified Electrostatic Gripper System for Aerial Object Manipulation","authors":"Dario Tscholl, Stephan-Daniel Gravert, Aurel X. Appius, Robert K. Katzschmann","doi":"10.48550/arXiv.2205.13011","DOIUrl":"https://doi.org/10.48550/arXiv.2205.13011","url":null,"abstract":"Rapid and versatile object manipulation in air is an open challenge. An energy-efficient and adaptive soft gripper combined with an agile aerial vehicle could revolutionize aerial robotic manipulation in areas such as warehousing. This paper presents a bio-inspired gripper powered by hydraulically amplified electrostatic actuators mounted to a quadcopter that can interact safely and naturally with its environment. Our gripping concept is motivated by an eagle's foot. Our custom multi-actuator concept is inspired by a scorpion tail design (consisting of a base electrode with pouches stacked adjacently) and spider-inspired joints (classic pouch motors with a flexible hinge layer). A hybrid of these two designs realizes a higher force output under moderate deflections of up to 25{deg} compared to single-hinge concepts. In addition, sandwiching the hinge layer improves the robustness of the gripper. For the first time, we show that soft manipulation in air is possible using electrostatic actuation. This study demonstrates the potential of untethered hydraulically amplified actuators in aerial robotic manipulation. Our proof of concept opens up the use of hydraulic electrostatic actuators in mobile aerial systems.","PeriodicalId":136210,"journal":{"name":"International Symposium of Robotics Research","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132585710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Safe, Occlusion-Aware Manipulation for Online Object Reconstruction in Confined Spaces 安全，闭塞感知操作在线对象重建在密闭空间

International Symposium of Robotics Research Pub Date : 2022-05-24 DOI: 10.48550/arXiv.2205.11719

Yinglong Miao, Rui Wang, Kostas E. Bekris

引用次数: 2

Learning Long-Horizon Robot Exploration Strategies for Multi-Object Search in Continuous Action Spaces 连续动作空间中多目标搜索的机器人长视界探索策略学习

International Symposium of Robotics Research Pub Date : 2022-05-23 DOI: 10.48550/arXiv.2205.11384

F. Schmalstieg, Daniel Honerkamp, T. Welschehold, A. Valada

引用次数: 6

CALIPSO: A Differentiable Solver for Trajectory Optimization with Conic and Complementarity Constraints 具有二次约束和互补约束的轨迹优化的可微求解器

International Symposium of Robotics Research Pub Date : 2022-05-19 DOI: 10.48550/arXiv.2205.09255

Taylor A. Howell, Simon Le Cleac'h, K. Tracy, Zachary Manchester

引用次数: 5

Robust-RRT: Probabilistically-Complete Motion Planning for Uncertain Nonlinear Systems 鲁棒- rrt:不确定非线性系统的概率完全运动规划

International Symposium of Robotics Research Pub Date : 2022-05-16 DOI: 10.48550/arXiv.2205.07728

A. Wu, T. Lew, Kiril Solovey, E. Schmerling, M. Pavone

{"title":"Robust-RRT: Probabilistically-Complete Motion Planning for Uncertain Nonlinear Systems","authors":"A. Wu, T. Lew, Kiril Solovey, E. Schmerling, M. Pavone","doi":"10.48550/arXiv.2205.07728","DOIUrl":"https://doi.org/10.48550/arXiv.2205.07728","url":null,"abstract":"Robust motion planning entails computing a global motion plan that is safe under all possible uncertainty realizations, be it in the system dynamics, the robot's initial position, or with respect to external disturbances. Current approaches for robust motion planning either lack theoretical guarantees, or make restrictive assumptions on the system dynamics and uncertainty distributions. In this paper, we address these limitations by proposing the robust rapidly-exploring random-tree (Robust-RRT) algorithm, which integrates forward reachability analysis directly into sampling-based control trajectory synthesis. We prove that Robust-RRT is probabilistically complete (PC) for nonlinear Lipschitz continuous dynamical systems with bounded uncertainty. In other words, Robust-RRT eventually finds a robust motion plan that is feasible under all possible uncertainty realizations assuming such a plan exists. Our analysis applies even to unstable systems that admit only short-horizon feasible plans; this is because we explicitly consider the time evolution of reachable sets along control trajectories. Thanks to the explicit consideration of time dependency in our analysis, PC applies to unstabilizable systems. To the best of our knowledge, this is the most general PC proof for robust sampling-based motion planning, in terms of the types of uncertainties and dynamical systems it can handle. Considering that an exact computation of reachable sets can be computationally expensive for some dynamical systems, we incorporate sampling-based reachability analysis into Robust-RRT and demonstrate our robust planner on nonlinear, underactuated, and hybrid systems.","PeriodicalId":136210,"journal":{"name":"International Symposium of Robotics Research","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123545061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3