arXiv - CS - Robotics最新文献_第6页

DroneDiffusion: Robust Quadrotor Dynamics Learning with Diffusion Models DroneDiffusion：利用扩散模型进行稳健的四旋翼动态学习

arXiv - CS - Robotics Pub Date : 2024-09-17 DOI: arxiv-2409.11292

Avirup Das, Rishabh Dev Yadav, Sihao Sun, Mingfei Sun, Samuel Kaski, Wei Pan

{"title":"DroneDiffusion: Robust Quadrotor Dynamics Learning with Diffusion Models","authors":"Avirup Das, Rishabh Dev Yadav, Sihao Sun, Mingfei Sun, Samuel Kaski, Wei Pan","doi":"arxiv-2409.11292","DOIUrl":"https://doi.org/arxiv-2409.11292","url":null,"abstract":"An inherent fragility of quadrotor systems stems from model inaccuracies and\u0000external disturbances. These factors hinder performance and compromise the\u0000stability of the system, making precise control challenging. Existing\u0000model-based approaches either make deterministic assumptions, utilize\u0000Gaussian-based representations of uncertainty, or rely on nominal models, all\u0000of which often fall short in capturing the complex, multimodal nature of\u0000real-world dynamics. This work introduces DroneDiffusion, a novel framework\u0000that leverages conditional diffusion models to learn quadrotor dynamics,\u0000formulated as a sequence generation task. DroneDiffusion achieves superior\u0000generalization to unseen, complex scenarios by capturing the temporal nature of\u0000uncertainties and mitigating error propagation. We integrate the learned\u0000dynamics with an adaptive controller for trajectory tracking with stability\u0000guarantees. Extensive experiments in both simulation and real-world flights\u0000demonstrate the robustness of the framework across a range of scenarios,\u0000including unfamiliar flight paths and varying payloads, velocities, and wind\u0000disturbances.","PeriodicalId":501031,"journal":{"name":"arXiv - CS - Robotics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142266980","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

VertiEncoder: Self-Supervised Kinodynamic Representation Learning on Vertically Challenging Terrain VertiEncoder：在垂直挑战性地形上进行自我监督的动力学表征学习

arXiv - CS - Robotics Pub Date : 2024-09-17 DOI: arxiv-2409.11570

Mohammad Nazeri, Aniket Datar, Anuj Pokhrel, Chenhui Pan, Garrett Warnell, Xuesu Xiao

引用次数: 0

Optimization of Rulebooks via Asymptotically Representing Lexicographic Hierarchies for Autonomous Vehicles 通过渐近表示自动驾驶汽车的词典层次优化规则手册

arXiv - CS - Robotics Pub Date : 2024-09-17 DOI: arxiv-2409.11199

Matteo Penlington, Alessandro Zanardi, Emilio Frazzoli

{"title":"Optimization of Rulebooks via Asymptotically Representing Lexicographic Hierarchies for Autonomous Vehicles","authors":"Matteo Penlington, Alessandro Zanardi, Emilio Frazzoli","doi":"arxiv-2409.11199","DOIUrl":"https://doi.org/arxiv-2409.11199","url":null,"abstract":"A key challenge in autonomous driving is that Autonomous Vehicles (AVs) must\u0000contend with multiple, often conflicting, planning requirements. These\u0000requirements naturally form in a hierarchy -- e.g., avoiding a collision is\u0000more important than maintaining lane. While the exact structure of this\u0000hierarchy remains unknown, to progress towards ensuring that AVs satisfy\u0000pre-determined behavior specifications, it is crucial to develop approaches\u0000that systematically account for it. Motivated by lexicographic behavior\u0000specification in AVs, this work addresses a lexicographic multi-objective\u0000motion planning problem, where each objective is incomparably more important\u0000than the next -- consider that avoiding a collision is incomparably more\u0000important than a lane change violation. This work ties together two elements.\u0000Firstly, a multi-objective candidate function that asymptotically represents\u0000lexicographic orders is introduced. Unlike existing multi-objective cost\u0000function formulations, this approach assures that returned solutions\u0000asymptotically align with the lexicographic behavior specification. Secondly,\u0000inspired by continuation methods, we propose two algorithms that asymptotically\u0000approach minimum rank decisions -- i.e., decisions that satisfy the highest\u0000number of important rules possible. Through a couple practical examples, we\u0000showcase that the proposed candidate function asymptotically represents the\u0000lexicographic hierarchy, and that both proposed algorithms return minimum rank\u0000decisions, even when other approaches do not.","PeriodicalId":501031,"journal":{"name":"arXiv - CS - Robotics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142266984","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

Context-Generative Default Policy for Bounded Rational Agent 有界理性代理的情境生成默认策略

arXiv - CS - Robotics Pub Date : 2024-09-17 DOI: arxiv-2409.11604

Durgakant Pushp, Junhong Xu, Zheng Chen, Lantao Liu

{"title":"Context-Generative Default Policy for Bounded Rational Agent","authors":"Durgakant Pushp, Junhong Xu, Zheng Chen, Lantao Liu","doi":"arxiv-2409.11604","DOIUrl":"https://doi.org/arxiv-2409.11604","url":null,"abstract":"Bounded rational agents often make decisions by evaluating a finite selection\u0000of choices, typically derived from a reference point termed the $`$default\u0000policy,' based on previous experience. However, the inherent rigidity of the\u0000static default policy presents significant challenges for agents when operating\u0000in unknown environment, that are not included in agent's prior knowledge. In\u0000this work, we introduce a context-generative default policy that leverages the\u0000region observed by the robot to predict unobserved part of the environment,\u0000thereby enabling the robot to adaptively adjust its default policy based on\u0000both the actual observed map and the $textit{imagined}$ unobserved map.\u0000Furthermore, the adaptive nature of the bounded rationality framework enables\u0000the robot to manage unreliable or incorrect imaginations by selectively\u0000sampling a few trajectories in the vicinity of the default policy. Our approach\u0000utilizes a diffusion model for map prediction and a sampling-based planning\u0000with B-spline trajectory optimization to generate the default policy. Extensive\u0000evaluations reveal that the context-generative policy outperforms the baseline\u0000methods in identifying and avoiding unseen obstacles. Additionally, real-world\u0000experiments conducted with the Crazyflie drones demonstrate the adaptability of\u0000our proposed method, even when acting in environments outside the domain of the\u0000training distribution.","PeriodicalId":501031,"journal":{"name":"arXiv - CS - Robotics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142266862","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

PLATO: Planning with LLMs and Affordances for Tool Manipulation PLATO：利用 LLM 和 Affordances 进行工具操作规划

arXiv - CS - Robotics Pub Date : 2024-09-17 DOI: arxiv-2409.11580

Arvind Car, Sai Sravan Yarlagadda, Alison Bartsch, Abraham George, Amir Barati Farimani

{"title":"PLATO: Planning with LLMs and Affordances for Tool Manipulation","authors":"Arvind Car, Sai Sravan Yarlagadda, Alison Bartsch, Abraham George, Amir Barati Farimani","doi":"arxiv-2409.11580","DOIUrl":"https://doi.org/arxiv-2409.11580","url":null,"abstract":"As robotic systems become increasingly integrated into complex real-world\u0000environments, there is a growing need for approaches that enable robots to\u0000understand and act upon natural language instructions without relying on\u0000extensive pre-programmed knowledge of their surroundings. This paper presents\u0000PLATO, an innovative system that addresses this challenge by leveraging\u0000specialized large language model agents to process natural language inputs,\u0000understand the environment, predict tool affordances, and generate executable\u0000actions for robotic systems. Unlike traditional systems that depend on\u0000hard-coded environmental information, PLATO employs a modular architecture of\u0000specialized agents to operate without any initial knowledge of the environment.\u0000These agents identify objects and their locations within the scene, generate a\u0000comprehensive high-level plan, translate this plan into a series of low-level\u0000actions, and verify the completion of each step. The system is particularly\u0000tested on challenging tool-use tasks, which involve handling diverse objects\u0000and require long-horizon planning. PLATO's design allows it to adapt to dynamic\u0000and unstructured settings, significantly enhancing its flexibility and\u0000robustness. By evaluating the system across various complex scenarios, we\u0000demonstrate its capability to tackle a diverse range of tasks and offer a novel\u0000solution to integrate LLMs with robotic platforms, advancing the\u0000state-of-the-art in autonomous robotic task execution. For videos and prompt\u0000details, please see our project website:\u0000https://sites.google.com/andrew.cmu.edu/plato","PeriodicalId":501031,"journal":{"name":"arXiv - CS - Robotics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142266863","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

P-RAG: Progressive Retrieval Augmented Generation For Planning on Embodied Everyday Task P-RAG：用于规划日常任务的渐进式检索增强生成技术

arXiv - CS - Robotics Pub Date : 2024-09-17 DOI: arxiv-2409.11279

Weiye Xu, Min Wang, Wengang Zhou, Houqiang Li

{"title":"P-RAG: Progressive Retrieval Augmented Generation For Planning on Embodied Everyday Task","authors":"Weiye Xu, Min Wang, Wengang Zhou, Houqiang Li","doi":"arxiv-2409.11279","DOIUrl":"https://doi.org/arxiv-2409.11279","url":null,"abstract":"Embodied Everyday Task is a popular task in the embodied AI community,\u0000requiring agents to make a sequence of actions based on natural language\u0000instructions and visual observations. Traditional learning-based approaches\u0000face two challenges. Firstly, natural language instructions often lack explicit\u0000task planning. Secondly, extensive training is required to equip models with\u0000knowledge of the task environment. Previous works based on Large Language Model\u0000(LLM) either suffer from poor performance due to the lack of task-specific\u0000knowledge or rely on ground truth as few-shot samples. To address the above\u0000limitations, we propose a novel approach called Progressive Retrieval Augmented\u0000Generation (P-RAG), which not only effectively leverages the powerful language\u0000processing capabilities of LLMs but also progressively accumulates\u0000task-specific knowledge without ground-truth. Compared to the conventional RAG\u0000methods, which retrieve relevant information from the database in a one-shot\u0000manner to assist generation, P-RAG introduces an iterative approach to\u0000progressively update the database. In each iteration, P-RAG retrieves the\u0000latest database and obtains historical information from the previous\u0000interaction as experiential references for the current interaction. Moreover,\u0000we also introduce a more granular retrieval scheme that not only retrieves\u0000similar tasks but also incorporates retrieval of similar situations to provide\u0000more valuable reference experiences. Extensive experiments reveal that P-RAG\u0000achieves competitive results without utilizing ground truth and can even\u0000further improve performance through self-iterations.","PeriodicalId":501031,"journal":{"name":"arXiv - CS - Robotics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142266981","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

Exact Wavefront Propagation for Globally Optimal One-to-All Path Planning on 2D Cartesian Grids 在二维笛卡尔网格上进行全局最优 "一对全 "路径规划的精确波前传播

arXiv - CS - Robotics Pub Date : 2024-09-17 DOI: arxiv-2409.11545

Ibrahim Ibrahim, Joris Gillis, Wilm Decré, Jan Swevers

引用次数: 0

Autonomous Navigation in Ice-Covered Waters with Learned Predictions on Ship-Ice Interactions 利用船舶与冰相互作用的学习预测在冰封水域自主导航

arXiv - CS - Robotics Pub Date : 2024-09-17 DOI: arxiv-2409.11326

Ninghan Zhong, Alessandro Potenza, Stephen L. Smith

引用次数: 0

PC-SRIF: Preconditioned Cholesky-based Square Root Information Filter for Vision-aided Inertial Navigation PC-SRIF：用于视觉辅助惯性导航的基于 Cholesky 的预条件平方根信息滤波器

arXiv - CS - Robotics Pub Date : 2024-09-17 DOI: arxiv-2409.11372

Tong Ke, Parth Agrawal, Yun Zhang, Weikun Zhen, Chao X. Guo, Toby Sharp, Ryan C. Dutoit

{"title":"PC-SRIF: Preconditioned Cholesky-based Square Root Information Filter for Vision-aided Inertial Navigation","authors":"Tong Ke, Parth Agrawal, Yun Zhang, Weikun Zhen, Chao X. Guo, Toby Sharp, Ryan C. Dutoit","doi":"arxiv-2409.11372","DOIUrl":"https://doi.org/arxiv-2409.11372","url":null,"abstract":"In this paper, we introduce a novel estimator for vision-aided inertial\u0000navigation systems (VINS), the Preconditioned Cholesky-based Square Root\u0000Information Filter (PC-SRIF). When solving linear systems, employing Cholesky\u0000decomposition offers superior efficiency but can compromise numerical\u0000stability. Due to this, existing VINS utilizing (Square Root) Information\u0000Filters often opt for QR decomposition on platforms where single precision is\u0000preferred, avoiding the numerical challenges associated with Cholesky\u0000decomposition. While these issues are often attributed to the ill-conditioned\u0000information matrix in VINS, our analysis reveals that this is not an inherent\u0000property of VINS but rather a consequence of specific parameterizations. We\u0000identify several factors that contribute to an ill-conditioned information\u0000matrix and propose a preconditioning technique to mitigate these conditioning\u0000issues. Building on this analysis, we present PC-SRIF, which exhibits\u0000remarkable stability in performing Cholesky decomposition in single precision\u0000when solving linear systems in VINS. Consequently, PC-SRIF achieves superior\u0000theoretical efficiency compared to alternative estimators. To validate the\u0000efficiency advantages and numerical stability of PC-SRIF based VINS, we have\u0000conducted well controlled experiments, which provide empirical evidence in\u0000support of our theoretical findings. Remarkably, in our VINS implementation,\u0000PC-SRIF's runtime is 41% faster than QR-based SRIF.","PeriodicalId":501031,"journal":{"name":"arXiv - CS - Robotics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142266870","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

Hyper-SAMARL: Hypergraph-based Coordinated Task Allocation and Socially-aware Navigation for Multi-Robot Systems Hyper-SAMARL：基于超图的多机器人系统协调任务分配和社会感知导航

arXiv - CS - Robotics Pub Date : 2024-09-17 DOI: arxiv-2409.11561

Weizheng Wang, Aniket Bera, Byung-Cheol Min

引用次数: 0