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

Optimal virtual tube planning and control for swarm robotics 群机器人的最优虚拟管规划与控制

1区计算机科学

International Journal of Robotics Research Pub Date : 2023-11-07 DOI: 10.1177/02783649231210012

Pengda Mao, Rao Fu, Quan Quan

{"title":"Optimal virtual tube planning and control for swarm robotics","authors":"Pengda Mao, Rao Fu, Quan Quan","doi":"10.1177/02783649231210012","DOIUrl":"https://doi.org/10.1177/02783649231210012","url":null,"abstract":"This paper presents a novel method for efficiently solving a trajectory planning problem for swarm robotics in cluttered environments. Recent research has demonstrated high success rates in real-time local trajectory planning for swarm robotics in cluttered environments, but optimizing trajectories for each robot is still computationally expensive, with a computational complexity from [Formula: see text] to [Formula: see text] where [Formula: see text] is the number of parameters in the parameterized trajectory, [Formula: see text] is precision, and [Formula: see text] is the number of iterations with respect to [Formula: see text] and [Formula: see text]. Furthermore, the swarm is difficult to move as a group. To address this issue, we define and then construct the optimal virtual tube, which includes infinite optimal trajectories. Under certain conditions, any optimal trajectory in the optimal virtual tube can be expressed as a convex combination of a finite number of optimal trajectories, with a computational complexity of [Formula: see text]. Afterward, a hierarchical approach including a planning method of the optimal virtual tube with minimizing energy and distributed model predictive control is proposed. In simulations and experiments, the proposed approach is validated and its effectiveness over other methods is demonstrated through comparison.","PeriodicalId":54942,"journal":{"name":"International Journal of Robotics Research","volume":"293 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135474810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exceeding traditional curvature limits of concentric tube robots through redundancy resolution 通过冗余度分辨率，突破了传统同心圆管机器人曲率极限

1区计算机科学

International Journal of Robotics Research Pub Date : 2023-11-07 DOI: 10.1177/02783649231202548

Patrick L Anderson, Richard J Hendrick, Margaret F Rox, Robert J Webster

引用次数: 2

Active preference-based Gaussian process regression for reward learning and optimization 基于主动偏好的高斯过程回归奖励学习与优化

1区计算机科学

International Journal of Robotics Research Pub Date : 2023-11-07 DOI: 10.1177/02783649231208729

Erdem Bıyık, Nicolas Huynh, Mykel J. Kochenderfer, Dorsa Sadigh

{"title":"Active preference-based Gaussian process regression for reward learning and optimization","authors":"Erdem Bıyık, Nicolas Huynh, Mykel J. Kochenderfer, Dorsa Sadigh","doi":"10.1177/02783649231208729","DOIUrl":"https://doi.org/10.1177/02783649231208729","url":null,"abstract":"Designing reward functions is a difficult task in AI and robotics. The complex task of directly specifying all the desirable behaviors a robot needs to optimize often proves challenging for humans. A popular solution is to learn reward functions using expert demonstrations. This approach, however, is fraught with many challenges. Some methods require heavily structured models, for example, reward functions that are linear in some predefined set of features, while others adopt less structured reward functions that may necessitate tremendous amounts of data. Moreover, it is difficult for humans to provide demonstrations on robots with high degrees of freedom, or even quantifying reward values for given trajectories. To address these challenges, we present a preference-based learning approach, where human feedback is in the form of comparisons between trajectories. We do not assume highly constrained structures on the reward function. Instead, we employ a Gaussian process to model the reward function and propose a mathematical formulation to actively fit the model using only human preferences. Our approach enables us to tackle both inflexibility and data-inefficiency problems within a preference-based learning framework. We further analyze our algorithm in comparison to several baselines on reward optimization, where the goal is to find the optimal robot trajectory in a data-efficient way instead of learning the reward function for every possible trajectory. Our results in three different simulation experiments and a user study show our approach can efficiently learn expressive reward functions for robotic tasks, and outperform the baselines in both reward learning and reward optimization.","PeriodicalId":54942,"journal":{"name":"International Journal of Robotics Research","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135475859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The “Fluid Jacobian”: Modeling force-motion relationships in fluid-driven soft robots “流体雅可比矩阵”:流体驱动软机器人的力-运动关系建模

1区计算机科学

International Journal of Robotics Research Pub Date : 2023-11-06 DOI: 10.1177/02783649231210592

C. David Remy, Zachary Brei, Daniel Bruder, Jan Remy, Keith Buffinton, R. Brent Gillespie

引用次数: 0

Certified polyhedral decompositions of collision-free configuration space 无碰撞位形空间的认证多面体分解

1区计算机科学

International Journal of Robotics Research Pub Date : 2023-11-03 DOI: 10.1177/02783649231201437

Hongkai Dai, Alexandre Amice, Peter Werner, Annan Zhang, Russ Tedrake

{"title":"Certified polyhedral decompositions of collision-free configuration space","authors":"Hongkai Dai, Alexandre Amice, Peter Werner, Annan Zhang, Russ Tedrake","doi":"10.1177/02783649231201437","DOIUrl":"https://doi.org/10.1177/02783649231201437","url":null,"abstract":"Understanding the geometry of collision-free configuration space (C-free) in the presence of Cartesian-space obstacles is an essential ingredient for collision-free motion planning. While it is possible to check for collisions at a point using standard algorithms, to date no practical method exists for computing C-free regions with rigorous certificates due to the complexity of mapping Cartesian-space obstacles through the kinematics. In this work, we present the first to our knowledge rigorous method for approximately decomposing a rational parametrization of C-free into certified polyhedral regions. Our method, called C-Iris (C-space Iterative Regional Inflation by Semidefinite programming), generates large, convex polytopes in a rational parameterization of the configuration space which are rigorously certified to be collision-free. Such regions have been shown to be useful for both optimization-based and randomized motion planning. Based on convex optimization, our method works in arbitrary dimensions, only makes assumptions about the convexity of the obstacles in the 3D Cartesian space, and is fast enough to scale to realistic problems in manipulation. We demonstrate our algorithm’s ability to fill a non-trivial amount of collision-free C-space in several 2-DOF examples where the C-space can be visualized, as well as the scalability of our algorithm on a 7-DOF KUKA iiwa, a 6-DOF UR3e, and 12-DOF bimanual manipulators. An implementation of our algorithm is open-sourced in Drake . We furthermore provide examples of our algorithm in interactive Python notebooks .","PeriodicalId":54942,"journal":{"name":"International Journal of Robotics Research","volume":"5 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135873889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Nonverbal social behavior generation for social robots using end-to-end learning 基于端到端学习的社交机器人非语言社交行为生成

1区计算机科学

International Journal of Robotics Research Pub Date : 2023-11-02 DOI: 10.1177/02783649231207974

Woo-Ri Ko, Minsu Jang, Jaeyeon Lee, Jaehong Kim

{"title":"Nonverbal social behavior generation for social robots using end-to-end learning","authors":"Woo-Ri Ko, Minsu Jang, Jaeyeon Lee, Jaehong Kim","doi":"10.1177/02783649231207974","DOIUrl":"https://doi.org/10.1177/02783649231207974","url":null,"abstract":"Social robots facilitate improved human–robot interactions through nonverbal behaviors such as handshakes or hugs. However, the traditional methods, which rely on precoded motions, are predictable and can detract from the perception of robots as interactive agents. To address this issue, we have introduced a Seq2Seq-based neural network model that learns social behaviors from human–human interactions in an end-to-end manner. To mitigate the risk of invalid pose sequences during long-term behavior generation, we incorporated a generative adversarial network (GAN). This proposed method was tested using the humanoid robot, Pepper, in a simulated environment. Given the challenges in assessing the success of social behavior generation, we devised novel metrics to quantify the discrepancy between the generated and ground-truth behaviors. Our analysis reveals the impact of different networks on behavior generation performance and compares the efficacy of learning multiple behaviors versus a single behavior. We anticipate that our method will find application in various sectors, including home service, guide, delivery, educational, and virtual robots, thereby enhancing user interaction and enjoyment.","PeriodicalId":54942,"journal":{"name":"International Journal of Robotics Research","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135875571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Quatro++: Robust global registration exploiting ground segmentation for loop closing in LiDAR SLAM 在激光雷达SLAM中利用地面分割进行闭环闭合的鲁棒全球配准

1区计算机科学

International Journal of Robotics Research Pub Date : 2023-11-02 DOI: 10.1177/02783649231207654

Hyungtae Lim, Beomsoo Kim, Daebeom Kim, Eungchang Mason Lee, Hyun Myung

{"title":"Quatro++: Robust global registration exploiting ground segmentation for loop closing in LiDAR SLAM","authors":"Hyungtae Lim, Beomsoo Kim, Daebeom Kim, Eungchang Mason Lee, Hyun Myung","doi":"10.1177/02783649231207654","DOIUrl":"https://doi.org/10.1177/02783649231207654","url":null,"abstract":"Global registration is a fundamental task that estimates the relative pose between two viewpoints of 3D point clouds. However, there are two issues that degrade the performance of global registration in LiDAR SLAM: one is the sparsity issue and the other is degeneracy. The sparsity issue is caused by the sparse characteristics of the 3D point cloud measurements in a mechanically spinning LiDAR sensor. The degeneracy issue sometimes occurs because the outlier-rejection methods reject too many correspondences, leaving less than three inliers. These two issues have become more severe as the pose discrepancy between the two viewpoints of 3D point clouds becomes greater. To tackle these problems, we propose a robust global registration framework, called Quatro++. Extending our previous work that solely focused on the global registration itself, we address the robust global registration in terms of the loop closing in LiDAR SLAM. To this end, ground segmentation is exploited to achieve robust global registration. Through the experiments, we demonstrate that our proposed method shows a higher succeiasdsss rate than the state-of-the-art global registration methods, overcoming the sparsity and degeneracy issues. In addition, we show that ground segmentation asdasd asignificantly helps to idfdfsncrease the success rate for the ground vehicles. Finally, we apply our proposed method to the loop clossdasdlksajing modulasdse in LiDAR SLAM and confirm that the quality of the loop constraints is improved, showing more precise mapping results. Therefore, the experimental evidence corroborated the suitabilitiasdasdy of our method as an initial alignment in the loop closing. Our code is available at https://quatro-plusplus.github.io .","PeriodicalId":54942,"journal":{"name":"International Journal of Robotics Research","volume":"22 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135973131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A structured prediction approach for robot imitation learning 机器人模仿学习的结构化预测方法

1区计算机科学

International Journal of Robotics Research Pub Date : 2023-11-01 DOI: 10.1177/02783649231204656

Anqing Duan, Iason Batzianoulis, Raffaello Camoriano, Lorenzo Rosasco, Daniele Pucci, Aude Billard

{"title":"A structured prediction approach for robot imitation learning","authors":"Anqing Duan, Iason Batzianoulis, Raffaello Camoriano, Lorenzo Rosasco, Daniele Pucci, Aude Billard","doi":"10.1177/02783649231204656","DOIUrl":"https://doi.org/10.1177/02783649231204656","url":null,"abstract":"We propose a structured prediction approach for robot imitation learning from demonstrations. Among various tools for robot imitation learning, supervised learning has been observed to have a prominent role. Structured prediction is a form of supervised learning that enables learning models to operate on output spaces with complex structures. Through the lens of structured prediction, we show how robots can learn to imitate trajectories belonging to not only Euclidean spaces but also Riemannian manifolds. Exploiting ideas from information theory, we propose a class of loss functions based on the f-divergence to measure the information loss between the demonstrated and reproduced probabilistic trajectories. Different types of f-divergence will result in different policies, which we call imitation modes. Furthermore, our approach enables the incorporation of spatial and temporal trajectory modulation, which is necessary for robots to be adaptive to the change in working conditions. We benchmark our algorithm against state-of-the-art methods in terms of trajectory reproduction and adaptation. The quantitative evaluation shows that our approach outperforms other algorithms regarding both accuracy and efficiency. We also report real-world experimental results on learning manifold trajectories in a polishing task with a KUKA LWR robot arm, illustrating the effectiveness of our algorithmic framework.","PeriodicalId":54942,"journal":{"name":"International Journal of Robotics Research","volume":"97 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135372905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-robot, multi-sensor exploration of multifarious environments with full mission aerial autonomy 多机器人、多传感器探索多种环境，具有完全的空中自主任务

1区计算机科学

International Journal of Robotics Research Pub Date : 2023-10-31 DOI: 10.1177/02783649231203342

Graeme Best, Rohit Garg, John Keller, Geoffrey A. Hollinger, Sebastian Scherer

{"title":"Multi-robot, multi-sensor exploration of multifarious environments with full mission aerial autonomy","authors":"Graeme Best, Rohit Garg, John Keller, Geoffrey A. Hollinger, Sebastian Scherer","doi":"10.1177/02783649231203342","DOIUrl":"https://doi.org/10.1177/02783649231203342","url":null,"abstract":"We present a coordinated autonomy pipeline for multi-sensor exploration of confined environments. We simultaneously address four broad challenges that are typically overlooked in prior work: (a) make effective use of both range and vision sensing modalities, (b) perform this exploration across a wide range of environments, (c) be resilient to adverse events, and (d) execute this onboard teams of physical robots. Our solution centers around a behavior tree architecture, which adaptively switches between various behaviors involving coordinated exploration and responding to adverse events. Our exploration strategy exploits the benefits of both visual and range sensors with a generalized frontier-based exploration algorithm and an OpenVDB-based map processing pipeline. Our local planner utilizes a dynamically feasible trajectory library and a GPU-based Euclidean distance transform map to allow fast and safe navigation through both tight doorways and expansive spaces. The autonomy pipeline is evaluated with an extensive set of field experiments, with teams of up to three robots that fly up to 3 m/s and distances exceeding 1 km in confined spaces. We provide a summary of various field experiments and detail resilient behaviors that arose: maneuvering narrow doorways, adapting to unexpected environment changes, and emergency landing. Experiments are also detailed from the DARPA Subterranean Challenge, where our proposed autonomy pipeline contributed to us winning the “Most Sectors Explored” award. We provide an extended discussion of lessons learned, release software as open source, and present a video that illustrates our extensive field trials.","PeriodicalId":54942,"journal":{"name":"International Journal of Robotics Research","volume":"12 21","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135813224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DUEL: Depth visUal Ego-motion Learning for autonomous robot obstacle avoidance 决斗:自主机器人避障的深度视觉自我运动学习

1区计算机科学

International Journal of Robotics Research Pub Date : 2023-10-30 DOI: 10.1177/02783649231210325

Naiyao Wang, Bo Zhang, Haixu Chi, Hua Wang, Seán McLoone, Hongbo Liu

{"title":"DUEL: Depth visUal Ego-motion Learning for autonomous robot obstacle avoidance","authors":"Naiyao Wang, Bo Zhang, Haixu Chi, Hua Wang, Seán McLoone, Hongbo Liu","doi":"10.1177/02783649231210325","DOIUrl":"https://doi.org/10.1177/02783649231210325","url":null,"abstract":"Reliable obstacle avoidance, which is essential for safe autonomous robot interaction with the real world, raises various challenges such as difficulties with obstacle perception and latent factor cognition impacting multi-modal obstacle avoidance. In this paper, we propose a Depth visUal Ego-motion Learning (DUEL) model, consisting of a cognitive generation network, a policy decision network and a potential partition network, to learn autonomous obstacle avoidance from expert policies. The DUEL model takes advantage of binocular vision to perceive scene depth. This serves as the input to the cognitive generation network which generates obstacle avoidance policies by maximizing its causal entropy. The policy decision network then optimizes the generation of the policies referring to expert policies. The generated obstacle avoidance policies are simultaneously transferred to the potential partition network to capture the latent factors contained within expert policies and perform multi-modal obstacle avoidance. These three core networks iteratively optimize the multi-modal policies relying on causal entropy and mutual information theorems, which are proven theoretically. Experimental comparisons with state-of-the-art models on 7 metrics demonstrate the effectiveness of the DUEL model. It achieves the best performance with an average ADE (Average Displacement Error) of 0.29 and average FDE (Final Displacement Error) of 0.55 across five different scenarios. Results show that the DUEL model can maintain an average obstacle avoidance success rate of 97% for both simulated and real world scenarios with multiple obstacles, demonstrating its success at capturing latent factors from expert policies. Our source codes are available at https://github.com/ACoTAI/DUEL .","PeriodicalId":54942,"journal":{"name":"International Journal of Robotics Research","volume":"47 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136019443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0