2023 IEEE International Conference on Robotics and Automation (ICRA)最新文献_第8页

Collaborative Scheduling with Adaptation to Failure for Heterogeneous Robot Teams 基于故障适应的异构机器人团队协同调度

2023 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2023-05-29 DOI: 10.1109/ICRA48891.2023.10161502

Peng Gao, S. Siva, Anthony Micciche, Haotian Zhang

{"title":"Collaborative Scheduling with Adaptation to Failure for Heterogeneous Robot Teams","authors":"Peng Gao, S. Siva, Anthony Micciche, Haotian Zhang","doi":"10.1109/ICRA48891.2023.10161502","DOIUrl":"https://doi.org/10.1109/ICRA48891.2023.10161502","url":null,"abstract":"Collaborative scheduling is an essential ability for a team of heterogeneous robots to collaboratively complete complex tasks, e.g., in a multi-robot assembly application. To enable collaborative scheduling, two key problems should be addressed, including allocating tasks to heterogeneous robots and adapting to robot failures in order to guarantee the completion of all tasks. In this paper, we introduce a novel approach that integrates deep bipartite graph matching and imitation learning for heterogeneous robots to complete complex tasks as a team. Specifically, we use a graph attention network to represent attributes and relationships of the tasks. Then, we formulate collaborative scheduling with failure adaptation as a new deep learning-based bipartite graph matching problem, which learns a policy by imitation to determine task scheduling based on the reward of potential task schedules. During normal execution, our approach generates robot-task pairs as potential allocations. When a robot fails, our approach identifies not only individual robots but also subteams to replace the failed robot. We conduct extensive experiments to evaluate our approach in the scenarios of collaborative scheduling with robot failures. Experimental results show that our approach achieves promising, generalizable and scalable results on collaborative scheduling with robot failure adaptation.","PeriodicalId":360533,"journal":{"name":"2023 IEEE International Conference on Robotics and Automation (ICRA)","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122064743","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

GP-Frontier for Local Mapless Navigation 局部无地图导航的GP-Frontier

2023 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2023-05-29 DOI: 10.1109/ICRA48891.2023.10161230

Mahmoud Ali, Lantao Liu

引用次数: 2

Large-Scale Radar Localization using Online Public Maps 基于在线公共地图的大规模雷达定位

2023 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2023-05-29 DOI: 10.1109/ICRA48891.2023.10160730

Ziyang Hong, Y. Pétillot, Kaicheng Zhang, S. Xu, Sen Wang

引用次数: 0

Dealing with Sparse Rewards in Continuous Control Robotics via Heavy-Tailed Policy Optimization 基于重尾策略优化的连续控制机器人稀疏奖励处理

2023 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2023-05-29 DOI: 10.1109/ICRA48891.2023.10161186

Souradip Chakraborty, A. S. Bedi, K. Weerakoon, Prithvi Poddar, Alec Koppel, Pratap Tokekar, Dinesh Manocha

{"title":"Dealing with Sparse Rewards in Continuous Control Robotics via Heavy-Tailed Policy Optimization","authors":"Souradip Chakraborty, A. S. Bedi, K. Weerakoon, Prithvi Poddar, Alec Koppel, Pratap Tokekar, Dinesh Manocha","doi":"10.1109/ICRA48891.2023.10161186","DOIUrl":"https://doi.org/10.1109/ICRA48891.2023.10161186","url":null,"abstract":"In this paper, we present a novel Heavy-Tailed Stochastic Policy Gradient (HT-PSG) algorithm to deal with the challenges of sparse rewards in continuous control problems. Sparse rewards are common in continuous control robotics tasks such as manipulation and navigation and make the learning problem hard due to the non-trivial estimation of value functions over the state space. This demands either reward shaping or expert demonstrations for the sparse reward environment. However, obtaining high-quality demonstrations is quite expensive and sometimes even impossible. We propose a heavy-tailed policy parametrization along with a modified momentum-based policy gradient tracking scheme (HT-SPG) to induce a stable exploratory behavior in the algorithm. The proposed algorithm does not require access to expert demonstrations. We test the performance of HT-SPG on various benchmark tasks of continuous control with sparse rewards such as 1D Mario, Pathological Mountain Car, Sparse Pendulum in OpenAI Gym, and Sparse MuJoCo environments (Hopper-v2, Half-Cheetah, Walker-2D). We show consistent performance improvement across all tasks in terms of high average cumulative reward without requiring access to expert demonstrations. We further demonstrate that a navigation policy trained using HT-SPG can be easily transferred into a Clearpath Husky robot to perform real-world navigation tasks.","PeriodicalId":360533,"journal":{"name":"2023 IEEE International Conference on Robotics and Automation (ICRA)","volume":" 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120831211","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: 0

Decentralized Multi-agent Exploration with Limited Inter-agent Communications 有限智能体间通信的分散多智能体探索

2023 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2023-05-29 DOI: 10.1109/ICRA48891.2023.10160599

Hans He, Alec Koppel, A. S. Bedi, D. Stilwell, M. Farhood, Benjamin Biggs

{"title":"Decentralized Multi-agent Exploration with Limited Inter-agent Communications","authors":"Hans He, Alec Koppel, A. S. Bedi, D. Stilwell, M. Farhood, Benjamin Biggs","doi":"10.1109/ICRA48891.2023.10160599","DOIUrl":"https://doi.org/10.1109/ICRA48891.2023.10160599","url":null,"abstract":"We consider the problem of decentralized multiagent environmental learning through maximizing the joint information gain among a team of agents. Inspired by subsea applications where bandwidth is severely limited, we explicitly consider the challenge of restricted communication between agents. The environment is modeled as a Gaussian process (GP), and the global information gain maximization problem in a GP is a set-valued optimization problem involving all agents' locally acquired data. We develop a decentralized method to solve it based on decomposition of information gain and exchange of limited subsets of data between agents. A key technical novelty of our approach is that we formulate the incentives for information exchange among agents as a submodular set optimization problem in terms of the log-determinant of their local covariance matrices. Numerical experiments on real-world data demonstrate the ability of our algorithm to explore trade-off between objectives. In particular, we demonstrate favorable performance on mapping problems where both decentralized information gathering and limited information exchange are essential.","PeriodicalId":360533,"journal":{"name":"2023 IEEE International Conference on Robotics and Automation (ICRA)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124039164","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: 0

FOGL: Federated Object Grasping Learning FOGL:联邦对象抓取学习

2023 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2023-05-29 DOI: 10.1109/ICRA48891.2023.10161191

Seok–Kyu Kang, Changhyun Choi

引用次数: 0

Adaptive Optimal Electrical Resistance Tomography for Large-Area Tactile Sensing 自适应最优电阻层析成像大面积触觉传感

2023 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2023-05-29 DOI: 10.1109/ICRA48891.2023.10161048

Wendong Zheng, Huaping Liu, Di Guo, Wuqiang Yang

{"title":"Adaptive Optimal Electrical Resistance Tomography for Large-Area Tactile Sensing","authors":"Wendong Zheng, Huaping Liu, Di Guo, Wuqiang Yang","doi":"10.1109/ICRA48891.2023.10161048","DOIUrl":"https://doi.org/10.1109/ICRA48891.2023.10161048","url":null,"abstract":"It is critical to perceive physical contact for intelligent robots to safely interact in dynamic, unstructured environments. As physical contacts can occur at any location, a well-performing tactile sensing system should be able to deploy a large area on robotic surface. Some researchers have implemented large-area tactile sensors by using sensing arrays, but it is challenging to deploy many sensing elements. Electrical resistance tomography (ERT) has recently been introduced into tactile sensing to overcome some of the limitations with conventional tactile sensing arrays, and good results have been achieved for some robotic applications. However, a particular challenge is that spatial resolution is low. Although various attempts have been made to improve the performance of ERT-based tactile sensors, the intrinsic resolution issue remains unsolved. In this paper, we propose a novel adaptive optimal drive strategy for efficient ERT-based large-area tactile sensing for robotic applications, which can adaptively select the current injection and voltage measurement pattern for optimal tactile stimulus. In particular, regions of tactile contacts are preliminarily detected and localized by a base scanning pattern with only a few measurement data. According to this detected region, the adaptive strategy can select the optimal current injection and voltage measurement pattern to improve the sensing performance by maximizing the current density. To verify the effectiveness of the proposed strategy, the proposed method is comprehensively evaluated by simulation and experiments. The results revealed that the optimal strategy can effectively improve both spatial and temporal resolution.","PeriodicalId":360533,"journal":{"name":"2023 IEEE International Conference on Robotics and Automation (ICRA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126159640","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: 0

SDF-Based Graph Convolutional Q-Networks for Rearrangement of Multiple Objects 基于sdf的多目标重排图卷积q网络

2023 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2023-05-29 DOI: 10.1109/ICRA48891.2023.10161394

Hogun Kee, Minjae Kang, Dohyeong Kim, Jaegoo Choy, Songhwai Oh

引用次数: 0

Discrete-time model based control of soft manipulator with FBG sensing 基于离散时间模型的光纤光栅传感软机械臂控制

2023 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2023-05-29 DOI: 10.1109/ICRA48891.2023.10160743

Enrico Franco, Ayhan Aktas, S. Treratanakulchai, Arnau Garriga-Casanovas, A. Donder, F. Baena

引用次数: 0

Deep Reinforcement Learning for Autonomous Driving using High-Level Heterogeneous Graph Representations 基于高级异构图表示的自动驾驶深度强化学习

2023 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2023-05-29 DOI: 10.1109/ICRA48891.2023.10160762

M. Schier, Christoph Reinders, B. Rosenhahn

{"title":"Deep Reinforcement Learning for Autonomous Driving using High-Level Heterogeneous Graph Representations","authors":"M. Schier, Christoph Reinders, B. Rosenhahn","doi":"10.1109/ICRA48891.2023.10160762","DOIUrl":"https://doi.org/10.1109/ICRA48891.2023.10160762","url":null,"abstract":"Graph networks have recently been used for decision making in automated driving tasks for their ability to capture a variable number of traffic participants. Current high-level graph-based approaches, however, do not model the entire road network and thus must rely on handcrafted features for vehicle-to-vehicle edges encompassing the road topology indirectly. We propose an entity-relation framework that intuitively models the road network and the traffic participants in a heterogeneous graph, representing all relevant information. Our novel architecture transforms the heterogeneous road-vehicle graph into a simpler graph of homogeneous node and edge types to allow effective training for deep reinforcement learning while introducing minimal prior knowledge. Unlike previous approaches, the vehicle-to-vehicle edges of this reduced graph are fully learnable and can therefore encode traffic rules without explicit feature design, an important step towards a holistic reinforcement learning model for automated driving. We show that our proposed method outperforms precomputed handcrafted features on intersection scenarios while also learning the semantics of right-of-way rules.","PeriodicalId":360533,"journal":{"name":"2023 IEEE International Conference on Robotics and Automation (ICRA)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128542339","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