arXiv - CS - Robotics最新文献_第10页

General Methods for Evaluating Collision Probability of Different Types of Theta-phi Positioners 评估不同类型 Theta-phi 定位器碰撞概率的一般方法

arXiv - CS - Robotics Pub Date : 2024-09-11 DOI: arxiv-2409.07288

Baolong Chen, Jianping Wang, Zhigang Liu, Zengxiang Zhou, Hongzhuan Hu, Feifan Zhang

引用次数: 0

Electrokinetic Propulsion for Electronically Integrated Microscopic Robots 电子集成微观机器人的电动推进器

arXiv - CS - Robotics Pub Date : 2024-09-11 DOI: arxiv-2409.07293

Lucas C. Hanson, William H. Reinhardt, Scott Shrager, Tarunyaa Sivakumar, Marc Z. Miskin

{"title":"Electrokinetic Propulsion for Electronically Integrated Microscopic Robots","authors":"Lucas C. Hanson, William H. Reinhardt, Scott Shrager, Tarunyaa Sivakumar, Marc Z. Miskin","doi":"arxiv-2409.07293","DOIUrl":"https://doi.org/arxiv-2409.07293","url":null,"abstract":"Robots too small to see by eye have rapidly evolved in recent years thanks to\u0000the incorporation of on-board microelectronics. Semiconductor circuits have\u0000been used in microrobots capable of executing controlled wireless steering,\u0000prescribed legged gait patterns, and user-triggered transitions between digital\u0000states. Yet these promising new capabilities have come at the steep price of\u0000complicated fabrication. Even though circuit components can be reliably built\u0000by semiconductor foundries, currently available actuators for electronically\u0000integrated microrobots are built with intricate multi-step cleanroom protocols\u0000and use mechanisms like articulated legs or bubble generators that are hard to\u0000design and control. Here, we present a propulsion system for electronically\u0000integrated microrobots that can be built with a single step of lithographic\u0000processing, readily integrates with microelectronics thanks to low current/low\u0000voltage operation (1V, 10nA), and yields robots that swim at speeds over one\u0000body length per second. Inspired by work on micromotors, these robots generate\u0000electric fields in a surrounding fluid, and by extension propulsive\u0000electrokinetic flows. The underlying physics is captured by a model in which\u0000robot speed is proportional to applied current, making design and control\u0000straightforward. As proof, we build basic robots that use on-board circuits and\u0000a closed-loop optical control scheme to navigate waypoints and move in\u0000coordinated swarms. Broadly, solid-state propulsion clears the way for robust,\u0000easy to manufacture, electronically controlled microrobots that operate\u0000reliably over months to years.","PeriodicalId":501031,"journal":{"name":"arXiv - CS - Robotics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142222131","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

Online Decision MetaMorphFormer: A Casual Transformer-Based Reinforcement Learning Framework of Universal Embodied Intelligence 在线决策元变形器：基于随意变形器的通用嵌入式智能强化学习框架

arXiv - CS - Robotics Pub Date : 2024-09-11 DOI: arxiv-2409.07341

Luo Ji, Runji Lin

{"title":"Online Decision MetaMorphFormer: A Casual Transformer-Based Reinforcement Learning Framework of Universal Embodied Intelligence","authors":"Luo Ji, Runji Lin","doi":"arxiv-2409.07341","DOIUrl":"https://doi.org/arxiv-2409.07341","url":null,"abstract":"Interactive artificial intelligence in the motion control field is an\u0000interesting topic, especially when universal knowledge is adaptive to multiple\u0000tasks and universal environments. Despite there being increasing efforts in the\u0000field of Reinforcement Learning (RL) with the aid of transformers, most of them\u0000might be limited by the offline training pipeline, which prohibits exploration\u0000and generalization abilities. To address this limitation, we propose the\u0000framework of Online Decision MetaMorphFormer (ODM) which aims to achieve\u0000self-awareness, environment recognition, and action planning through a unified\u0000model architecture. Motivated by cognitive and behavioral psychology, an ODM\u0000agent is able to learn from others, recognize the world, and practice itself\u0000based on its own experience. ODM can also be applied to any arbitrary agent\u0000with a multi-joint body, located in different environments, and trained with\u0000different types of tasks using large-scale pre-trained datasets. Through the\u0000use of pre-trained datasets, ODM can quickly warm up and learn the necessary\u0000knowledge to perform the desired task, while the target environment continues\u0000to reinforce the universal policy. Extensive online experiments as well as\u0000few-shot and zero-shot environmental tests are used to verify ODM's performance\u0000and generalization ability. The results of our study contribute to the study of\u0000general artificial intelligence in embodied and cognitive fields. Code,\u0000results, and video examples can be found on the website\u0000url{https://rlodm.github.io/odm/}.","PeriodicalId":501031,"journal":{"name":"arXiv - CS - Robotics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142222177","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

Learning Task Specifications from Demonstrations as Probabilistic Automata 从作为概率自动机的演示中学习任务规范

arXiv - CS - Robotics Pub Date : 2024-09-11 DOI: arxiv-2409.07091

Mattijs Baert, Sam Leroux, Pieter Simoens

引用次数: 0

Unsupervised Point Cloud Registration with Self-Distillation 利用自扩散技术实现无监督点云注册

arXiv - CS - Robotics Pub Date : 2024-09-11 DOI: arxiv-2409.07558

Christian Löwens, Thorben Funke, André Wagner, Alexandru Paul Condurache

{"title":"Unsupervised Point Cloud Registration with Self-Distillation","authors":"Christian Löwens, Thorben Funke, André Wagner, Alexandru Paul Condurache","doi":"arxiv-2409.07558","DOIUrl":"https://doi.org/arxiv-2409.07558","url":null,"abstract":"Rigid point cloud registration is a fundamental problem and highly relevant\u0000in robotics and autonomous driving. Nowadays deep learning methods can be\u0000trained to match a pair of point clouds, given the transformation between them.\u0000However, this training is often not scalable due to the high cost of collecting\u0000ground truth poses. Therefore, we present a self-distillation approach to learn\u0000point cloud registration in an unsupervised fashion. Here, each sample is\u0000passed to a teacher network and an augmented view is passed to a student\u0000network. The teacher includes a trainable feature extractor and a learning-free\u0000robust solver such as RANSAC. The solver forces consistency among\u0000correspondences and optimizes for the unsupervised inlier ratio, eliminating\u0000the need for ground truth labels. Our approach simplifies the training\u0000procedure by removing the need for initial hand-crafted features or consecutive\u0000point cloud frames as seen in related methods. We show that our method not only\u0000surpasses them on the RGB-D benchmark 3DMatch but also generalizes well to\u0000automotive radar, where classical features adopted by others fail. The code is\u0000available at https://github.com/boschresearch/direg .","PeriodicalId":501031,"journal":{"name":"arXiv - CS - Robotics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142222128","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

Flow-Inspired Lightweight Multi-Robot Real-Time Scheduling Planner 受流程启发的轻量级多机器人实时调度规划器

arXiv - CS - Robotics Pub Date : 2024-09-11 DOI: arxiv-2409.06952

Han Liu, Yu Jin, Tianjiang Hu, Kai Huang

{"title":"Flow-Inspired Lightweight Multi-Robot Real-Time Scheduling Planner","authors":"Han Liu, Yu Jin, Tianjiang Hu, Kai Huang","doi":"arxiv-2409.06952","DOIUrl":"https://doi.org/arxiv-2409.06952","url":null,"abstract":"Collision avoidance and trajectory planning are crucial in multi-robot\u0000systems, particularly in environments with numerous obstacles. Although\u0000extensive research has been conducted in this field, the challenge of rapid\u0000traversal through such environments has not been fully addressed. This paper\u0000addresses this problem by proposing a novel real-time scheduling scheme\u0000designed to optimize the passage of multi-robot systems through complex,\u0000obstacle-rich maps. Inspired from network flow optimization, our scheme\u0000decomposes the environment into a network structure, enabling the efficient\u0000allocation of robots to paths based on real-time congestion data. The proposed\u0000scheduling planner operates on top of existing collision avoidance algorithms,\u0000focusing on minimizing traversal time by balancing robot detours and waiting\u0000times. Our simulation results demonstrate the efficiency of the proposed\u0000scheme. Additionally, we validated its effectiveness through real world flight\u0000tests using ten quadrotors. This work contributes a lightweight, effective\u0000scheduling planner capable of meeting the real-time demands of multi-robot\u0000systems in obstacle-rich environments.","PeriodicalId":501031,"journal":{"name":"arXiv - CS - Robotics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142222156","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

ODYSSEE: Oyster Detection Yielded by Sensor Systems on Edge Electronics ODYSSEE：边缘电子传感器系统产生的牡蛎探测结果

arXiv - CS - Robotics Pub Date : 2024-09-11 DOI: arxiv-2409.07003

Xiaomin Lin, Vivek Mange, Arjun Suresh, Bernhard Neuberger, Aadi Palnitkar, Brendan Campbell, Alan Williams, Kleio Baxevani, Jeremy Mallette, Alhim Vera, Markus Vincze, Ioannis Rekleitis, Herbert G. Tanner, Yiannis Aloimonos

{"title":"ODYSSEE: Oyster Detection Yielded by Sensor Systems on Edge Electronics","authors":"Xiaomin Lin, Vivek Mange, Arjun Suresh, Bernhard Neuberger, Aadi Palnitkar, Brendan Campbell, Alan Williams, Kleio Baxevani, Jeremy Mallette, Alhim Vera, Markus Vincze, Ioannis Rekleitis, Herbert G. Tanner, Yiannis Aloimonos","doi":"arxiv-2409.07003","DOIUrl":"https://doi.org/arxiv-2409.07003","url":null,"abstract":"Oysters are a keystone species in coastal ecosystems, offering significant\u0000economic, environmental, and cultural benefits. However, current monitoring\u0000systems are often destructive, typically involving dredging to physically\u0000collect and count oysters. A nondestructive alternative is manual\u0000identification from video footage collected by divers, which is time-consuming\u0000and labor-intensive with expert input. An alternative to human monitoring is the deployment of a system with trained\u0000object detection models that performs real-time, on edge oyster detection in\u0000the field. One such platform is the Aqua2 robot. Effective training of these\u0000models requires extensive high-quality data, which is difficult to obtain in\u0000marine settings. To address these complications, we introduce a novel method\u0000that leverages stable diffusion to generate high-quality synthetic data for the\u0000marine domain. We exploit diffusion models to create photorealistic marine\u0000imagery, using ControlNet inputs to ensure consistency with the segmentation\u0000ground-truth mask, the geometry of the scene, and the target domain of real\u0000underwater images for oysters. The resulting dataset is used to train a\u0000YOLOv10-based vision model, achieving a state-of-the-art 0.657 mAP@50 for\u0000oyster detection on the Aqua2 platform. The system we introduce not only\u0000improves oyster habitat monitoring, but also paves the way to autonomous\u0000surveillance for various tasks in marine contexts, improving aquaculture and\u0000conservation efforts.","PeriodicalId":501031,"journal":{"name":"arXiv - CS - Robotics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142222178","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

Compliant Blind Handover Control for Human-Robot Collaboration 用于人机协作的兼容盲切换控制

arXiv - CS - Robotics Pub Date : 2024-09-11 DOI: arxiv-2409.07155

Davide Ferrari, Andrea Pupa, Cristian Secchi

引用次数: 0

Learning Robotic Manipulation Policies from Point Clouds with Conditional Flow Matching 利用条件流匹配从点云学习机器人操纵策略

arXiv - CS - Robotics Pub Date : 2024-09-11 DOI: arxiv-2409.07343

Eugenio Chisari, Nick Heppert, Max Argus, Tim Welschehold, Thomas Brox, Abhinav Valada

{"title":"Learning Robotic Manipulation Policies from Point Clouds with Conditional Flow Matching","authors":"Eugenio Chisari, Nick Heppert, Max Argus, Tim Welschehold, Thomas Brox, Abhinav Valada","doi":"arxiv-2409.07343","DOIUrl":"https://doi.org/arxiv-2409.07343","url":null,"abstract":"Learning from expert demonstrations is a promising approach for training\u0000robotic manipulation policies from limited data. However, imitation learning\u0000algorithms require a number of design choices ranging from the input modality,\u0000training objective, and 6-DoF end-effector pose representation. Diffusion-based\u0000methods have gained popularity as they enable predicting long-horizon\u0000trajectories and handle multimodal action distributions. Recently, Conditional\u0000Flow Matching (CFM) (or Rectified Flow) has been proposed as a more flexible\u0000generalization of diffusion models. In this paper, we investigate the\u0000application of CFM in the context of robotic policy learning and specifically\u0000study the interplay with the other design choices required to build an\u0000imitation learning algorithm. We show that CFM gives the best performance when\u0000combined with point cloud input observations. Additionally, we study the\u0000feasibility of a CFM formulation on the SO(3) manifold and evaluate its\u0000suitability with a simplified example. We perform extensive experiments on\u0000RLBench which demonstrate that our proposed PointFlowMatch approach achieves a\u0000state-of-the-art average success rate of 67.8% over eight tasks, double the\u0000performance of the next best method.","PeriodicalId":501031,"journal":{"name":"arXiv - CS - Robotics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142222135","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

Mamba Policy: Towards Efficient 3D Diffusion Policy with Hybrid Selective State Models 曼巴政策：利用混合选择性状态模型实现高效的 3D 扩散策略

arXiv - CS - Robotics Pub Date : 2024-09-11 DOI: arxiv-2409.07163

Jiahang Cao, Qiang Zhang, Jingkai Sun, Jiaxu Wang, Hao Cheng, Yulin Li, Jun Ma, Yecheng Shao, Wen Zhao, Gang Han, Yijie Guo, Renjing Xu

{"title":"Mamba Policy: Towards Efficient 3D Diffusion Policy with Hybrid Selective State Models","authors":"Jiahang Cao, Qiang Zhang, Jingkai Sun, Jiaxu Wang, Hao Cheng, Yulin Li, Jun Ma, Yecheng Shao, Wen Zhao, Gang Han, Yijie Guo, Renjing Xu","doi":"arxiv-2409.07163","DOIUrl":"https://doi.org/arxiv-2409.07163","url":null,"abstract":"Diffusion models have been widely employed in the field of 3D manipulation\u0000due to their efficient capability to learn distributions, allowing for precise\u0000prediction of action trajectories. However, diffusion models typically rely on\u0000large parameter UNet backbones as policy networks, which can be challenging to\u0000deploy on resource-constrained devices. Recently, the Mamba model has emerged\u0000as a promising solution for efficient modeling, offering low computational\u0000complexity and strong performance in sequence modeling. In this work, we\u0000propose the Mamba Policy, a lighter but stronger policy that reduces the\u0000parameter count by over 80% compared to the original policy network while\u0000achieving superior performance. Specifically, we introduce the XMamba Block,\u0000which effectively integrates input information with conditional features and\u0000leverages a combination of Mamba and Attention mechanisms for deep feature\u0000extraction. Extensive experiments demonstrate that the Mamba Policy excels on\u0000the Adroit, Dexart, and MetaWorld datasets, requiring significantly fewer\u0000computational resources. Additionally, we highlight the Mamba Policy's enhanced\u0000robustness in long-horizon scenarios compared to baseline methods and explore\u0000the performance of various Mamba variants within the Mamba Policy framework.\u0000Our project page is in https://andycao1125.github.io/mamba_policy/.","PeriodicalId":501031,"journal":{"name":"arXiv - CS - Robotics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142222150","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