arXiv - CS - Robotics最新文献_第8页

InterACT: Inter-dependency Aware Action Chunking with Hierarchical Attention Transformers for Bimanual Manipulation InterACT：利用分层注意力转换器进行相互依赖感知的动作分块，用于双手操作

arXiv - CS - Robotics Pub Date : 2024-09-12 DOI: arxiv-2409.07914

Andrew Lee, Ian Chuang, Ling-Yuan Chen, Iman Soltani

引用次数: 0

Universal Trajectory Optimization Framework for Differential-Driven Robot Class 微分驱动机器人类的通用轨迹优化框架

arXiv - CS - Robotics Pub Date : 2024-09-12 DOI: arxiv-2409.07924

Mengke Zhang, Zhichao Han, Chao Xu, Fei Gao, Yanjun Cao

{"title":"Universal Trajectory Optimization Framework for Differential-Driven Robot Class","authors":"Mengke Zhang, Zhichao Han, Chao Xu, Fei Gao, Yanjun Cao","doi":"arxiv-2409.07924","DOIUrl":"https://doi.org/arxiv-2409.07924","url":null,"abstract":"Differential-driven robots are widely used in various scenarios thanks to\u0000their straightforward principle, from household service robots to disaster\u0000response field robots. There are several different types of deriving mechanisms\u0000considering the real-world applications, including two-wheeled, four-wheeled\u0000skid-steering, tracked robots, etc. The differences in the driving mechanism\u0000usually require specific kinematic modeling when precise controlling is\u0000desired. Furthermore, the nonholonomic dynamics and possible lateral slip lead\u0000to different degrees of difficulty in getting feasible and high-quality\u0000trajectories. Therefore, a comprehensive trajectory optimization framework to\u0000compute trajectories efficiently for various kinds of differential-driven\u0000robots is highly desirable. In this paper, we propose a universal trajectory\u0000optimization framework that can be applied to differential-driven robot class,\u0000enabling the generation of high-quality trajectories within a restricted\u0000computational timeframe. We introduce a novel trajectory representation based\u0000on polynomial parameterization of motion states or their integrals, such as\u0000angular and linear velocities, that inherently matching robots' motion to the\u0000control principle for differential-driven robot class. The trajectory\u0000optimization problem is formulated to minimize complexity while prioritizing\u0000safety and operational efficiency. We then build a full-stack autonomous\u0000planning and control system to show the feasibility and robustness. We conduct\u0000extensive simulations and real-world testing in crowded environments with three\u0000kinds of differential-driven robots to validate the effectiveness of our\u0000approach. We will release our method as an open-source package.","PeriodicalId":501031,"journal":{"name":"arXiv - CS - Robotics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142222096","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

Graph Inspection for Robotic Motion Planning: Do Arithmetic Circuits Help? 机器人运动规划的图形检测：算术电路有用吗？

arXiv - CS - Robotics Pub Date : 2024-09-12 DOI: arxiv-2409.08219

Matthias Bentert, Daniel Coimbra Salomao, Alex Crane, Yosuke Mizutani, Felix Reidl, Blair D. Sullivan

引用次数: 0

Towards Online Safety Corrections for Robotic Manipulation Policies 实现机器人操纵政策的在线安全修正

arXiv - CS - Robotics Pub Date : 2024-09-12 DOI: arxiv-2409.08233

Ariana Spalter, Mark Roberts, Laura M. Hiatt

引用次数: 0

Adaptive Language-Guided Abstraction from Contrastive Explanations 自适应语言引导下的对比解释抽象

arXiv - CS - Robotics Pub Date : 2024-09-12 DOI: arxiv-2409.08212

Andi Peng, Belinda Z. Li, Ilia Sucholutsky, Nishanth Kumar, Julie A. Shah, Jacob Andreas, Andreea Bobu

{"title":"Adaptive Language-Guided Abstraction from Contrastive Explanations","authors":"Andi Peng, Belinda Z. Li, Ilia Sucholutsky, Nishanth Kumar, Julie A. Shah, Jacob Andreas, Andreea Bobu","doi":"arxiv-2409.08212","DOIUrl":"https://doi.org/arxiv-2409.08212","url":null,"abstract":"Many approaches to robot learning begin by inferring a reward function from a\u0000set of human demonstrations. To learn a good reward, it is necessary to\u0000determine which features of the environment are relevant before determining how\u0000these features should be used to compute reward. End-to-end methods for joint\u0000feature and reward learning (e.g., using deep networks or program synthesis\u0000techniques) often yield brittle reward functions that are sensitive to spurious\u0000state features. By contrast, humans can often generalizably learn from a small\u0000number of demonstrations by incorporating strong priors about what features of\u0000a demonstration are likely meaningful for a task of interest. How do we build\u0000robots that leverage this kind of background knowledge when learning from new\u0000demonstrations? This paper describes a method named ALGAE (Adaptive\u0000Language-Guided Abstraction from [Contrastive] Explanations) which alternates\u0000between using language models to iteratively identify human-meaningful features\u0000needed to explain demonstrated behavior, then standard inverse reinforcement\u0000learning techniques to assign weights to these features. Experiments across a\u0000variety of both simulated and real-world robot environments show that ALGAE\u0000learns generalizable reward functions defined on interpretable features using\u0000only small numbers of demonstrations. Importantly, ALGAE can recognize when\u0000features are missing, then extract and define those features without any human\u0000input -- making it possible to quickly and efficiently acquire rich\u0000representations of user behavior.","PeriodicalId":501031,"journal":{"name":"arXiv - CS - Robotics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142222091","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

ReGentS: Real-World Safety-Critical Driving Scenario Generation Made Stable ReGentS：稳定生成真实世界的安全关键驾驶场景

arXiv - CS - Robotics Pub Date : 2024-09-12 DOI: arxiv-2409.07830

Yuan Yin, Pegah Khayatan, Éloi Zablocki, Alexandre Boulch, Matthieu Cord

引用次数: 0

Hand-Object Interaction Pretraining from Videos 通过视频进行手与物体交互预训练

arXiv - CS - Robotics Pub Date : 2024-09-12 DOI: arxiv-2409.08273

Himanshu Gaurav Singh, Antonio Loquercio, Carmelo Sferrazza, Jane Wu, Haozhi Qi, Pieter Abbeel, Jitendra Malik

引用次数: 0

Real-time Multi-view Omnidirectional Depth Estimation System for Robots and Autonomous Driving on Real Scenes 用于真实场景中机器人和自动驾驶汽车的实时多视角全方位深度估算系统

arXiv - CS - Robotics Pub Date : 2024-09-12 DOI: arxiv-2409.07843

Ming Li, Xiong Yang, Chaofan Wu, Jiaheng Li, Pinzhi Wang, Xuejiao Hu, Sidan Du, Yang Li

引用次数: 0

FIReStereo: Forest InfraRed Stereo Dataset for UAS Depth Perception in Visually Degraded Environments FIReStereo：用于视觉退化环境中无人机系统深度感知的森林红外立体数据集

arXiv - CS - Robotics Pub Date : 2024-09-12 DOI: arxiv-2409.07715

Devansh Dhrafani, Yifei Liu, Andrew Jong, Ukcheol Shin, Yao He, Tyler Harp, Yaoyu Hu, Jean Oh, Sebastian Scherer

{"title":"FIReStereo: Forest InfraRed Stereo Dataset for UAS Depth Perception in Visually Degraded Environments","authors":"Devansh Dhrafani, Yifei Liu, Andrew Jong, Ukcheol Shin, Yao He, Tyler Harp, Yaoyu Hu, Jean Oh, Sebastian Scherer","doi":"arxiv-2409.07715","DOIUrl":"https://doi.org/arxiv-2409.07715","url":null,"abstract":"Robust depth perception in visually-degraded environments is crucial for\u0000autonomous aerial systems. Thermal imaging cameras, which capture infrared\u0000radiation, are robust to visual degradation. However, due to lack of a\u0000large-scale dataset, the use of thermal cameras for unmanned aerial system\u0000(UAS) depth perception has remained largely unexplored. This paper presents a\u0000stereo thermal depth perception dataset for autonomous aerial perception\u0000applications. The dataset consists of stereo thermal images, LiDAR, IMU and\u0000ground truth depth maps captured in urban and forest settings under diverse\u0000conditions like day, night, rain, and smoke. We benchmark representative stereo\u0000depth estimation algorithms, offering insights into their performance in\u0000degraded conditions. Models trained on our dataset generalize well to unseen\u0000smoky conditions, highlighting the robustness of stereo thermal imaging for\u0000depth perception. We aim for this work to enhance robotic perception in\u0000disaster scenarios, allowing for exploration and operations in previously\u0000unreachable areas. The dataset and source code are available at\u0000https://firestereo.github.io.","PeriodicalId":501031,"journal":{"name":"arXiv - CS - Robotics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142222125","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

Relevance for Human Robot Collaboration 与人机协作的相关性

arXiv - CS - Robotics Pub Date : 2024-09-12 DOI: arxiv-2409.07753

Xiaotong Zhang, Dingcheng Huang, Kamal Youcef-Toumi

{"title":"Relevance for Human Robot Collaboration","authors":"Xiaotong Zhang, Dingcheng Huang, Kamal Youcef-Toumi","doi":"arxiv-2409.07753","DOIUrl":"https://doi.org/arxiv-2409.07753","url":null,"abstract":"Effective human-robot collaboration (HRC) requires the robots to possess\u0000human-like intelligence. Inspired by the human's cognitive ability to\u0000selectively process and filter elements in complex environments, this paper\u0000introduces a novel concept and scene-understanding approach termed `relevance.'\u0000It identifies relevant components in a scene. To accurately and efficiently\u0000quantify relevance, we developed an event-based framework that selectively\u0000triggers relevance determination, along with a probabilistic methodology built\u0000on a structured scene representation. Simulation results demonstrate that the\u0000relevance framework and methodology accurately predict the relevance of a\u0000general HRC setup, achieving a precision of 0.99 and a recall of 0.94.\u0000Relevance can be broadly applied to several areas in HRC to improve task\u0000planning time by 79.56% compared with pure planning for a cereal task, reduce\u0000perception latency by up to 26.53% for an object detector, improve HRC safety\u0000by up to 13.50% and reduce the number of inquiries for HRC by 75.36%. A\u0000real-world demonstration showcases the relevance framework's ability to\u0000intelligently assist humans in everyday tasks.","PeriodicalId":501031,"journal":{"name":"arXiv - CS - Robotics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142222099","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