The ability to autonomously navigate in unknown environments is important for mobile robots. The map is the core component to achieve this. Most map representations rely on drift-free state estimation and provide a global metric map to navigate. However, in large-scale real-world applications, it's hard to prohibit drifts and compose a globally consistent map quickly. In this paper, a novel representation named, HiTMap, is proposed to enhance the existing map representations. The central idea is to adopt a submap-based hierarchical topology rather than a global metric map so that only a local metric map is maintained for obstacle avoidance which ensures the lightweight of the representation. To guide the robots navigate into unknown spaces, frontiers are detected and attached to the map as an attribute. We also develop a path planning module to evaluate the feasibility and efficiency of our map representation. The system is validated in a simulation environment and a demonstration in the real world is conducted. In addition, the HiTMap is made available open-source.
Hierarchical Experience-informed Navigation for Multi-modal Quadrupedal Rebar Grid Traversal
Georgia Institute of Technology（佐治亚理工学院）；School of Mechanical Engineering, Georgia Institute of Technology（佐治亚理工学院机械工程学院）；SkyMul Inc（SkyMul公司）；School of Electrical and Computer Engineering, Georgia Institute of Technology（佐治亚理工学院电气与计算机工程学院）
Georgia Institute of Technology（佐治亚理工学院）；Carnegie Mellon University（卡内基梅隆大学）
This paper describes a hierarchical solution consisting of a multi-phase planner and a low-level safe controller to jointly solve the safe navigation problem in crowded, dynamic, and uncertain environments. The planner employs dynamic gap analysis and trajectory optimization to achieve collision avoidance with respect to the predicted trajectories of dynamic agents within the sensing and planning horizon and with robustness to agent uncertainty. To address uncertainty over the planning horizon and real-time safety, a fast reactive safe set algorithm (SSA) is adopted, which monitors and modifie…
UC San Diego（加州大学圣地亚哥分校）；NEC Labs America（NEC美国实验室）；University of Pennsylvania（宾夕法尼亚大学）
We address key challenges in long-horizon embodied exploration and navigation by proposing a new object transport task and a novel modular framework for temporally extended navigation. Our first contribution is the design of a novel Long-HOT environment focused on deep exploration and long-horizon planning where the agent is required to efficiently find and pick up target objects to be carried and dropped at a goal location, with load constraints and optional access to a container if it finds one. Further, we propose a modular hierarchical transport policy (HTP) that builds a topological graph…
HDPlanner: Advancing Autonomous Deployments in Unknown Environments through Hierarchical Decision Networks
National University of Singapore（新加坡国立大学）；College of Design and Engineering, National University of Singapore（新加坡国立大学设计与工程学院）；School of Computing, National University of Singapore（新加坡国立大学计算机学院）
PM-Nav: Priori-Map Guided Embodied Navigation in Functional Buildings
Faculty of Robot Science and Engineering at Northeastern University（东北大学机器人科学与工程学院）；Foshan Graduate School of Innovation at Northeastern University（东北大学佛山创新研究生学院）；School of Aeronautic Science and Engineering at Beihang University（北航航空科学与工程学院）
Robotic systems are nowadays capable of solving complex navigation tasks. However, their capabilities are limited to the knowledge of the designer and consequently lack generalizability to initially unconsidered situations. This makes deep reinforcement learning (DRL) especially interesting, as these algorithms promise a self-learning system only relying on feedback from the environment. In this paper, we consider the problem of lidar-based robot navigation in continuous action space using DRL without providing any goal-oriented or global information. By relying solely on local sensor data to …
Deep Learning has revolutionized our ability to solve complex problems such as Vision-and-Language Navigation (VLN). This task requires the agent to navigate to a goal purely based on visual sensory inputs given natural language instructions. However, prior works formulate the problem as a navigation graph with a discrete action space. In this work, we lift the agent off the navigation graph and propose a more complex VLN setting in continuous 3D reconstructed environments. Our proposed setting, Robo-VLN, more closely mimics the challenges of real world navigation. Robo-VLN tasks have longer t…
We introduce the use of hierarchical clustering for relaxed, deterministic coordination and control of multiple robots. Traditionally an unsupervised learning method, hierarchical clustering offers a formalism for identifying and representing spatially cohesive and segregated robot groups at different resolutions by relating the continuous space of configurations to the combinatorial space of trees. We formalize and exploit this relation, developing computationally effective reactive algorithms for navigating through the combinatorial space in concert with geometric realizations for a particul…
Data fusion has become an active research topic in recent years. Growing computational performance has allowed the use of redundant sensors to measure a single phenomenon. While Bayesian fusion approaches are common in general applications, the computer vision field has largely relegated this approach. Most object following algorithms have gone towards pure machine learning fusion techniques that tend to lack flexibility. Consequently, a more general data fusion scheme is needed. Within this work, a hierarchical Bayesian fusion approach is proposed, which outperforms individual trackers by usi…
Google Brain（谷歌大脑）；University of New Mexico（新墨西哥大学）
We present PRM-RL, a hierarchical method for long-range navigation task completion that combines sampling based path planning with reinforcement learning (RL). The RL agents learn short-range, point-to-point navigation policies that capture robot dynamics and task constraints without knowledge of the large-scale topology. Next, the sampling-based planners provide roadmaps which connect robot configurations that can be successfully navigated by the RL agent. The same RL agents are used to control the robot under the direction of the planning, enabling long-range navigation. We use the Probabili…
Think before Go: Hierarchical Reasoning for Image-goal Navigation
National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University（人机混合增强智能国家实验室、视觉信息与应用国家工程研究中心、人工智能与机器人研究所、西安交通大学）；University of Macau（澳门大学）；Xiaomi EV（小米电动车）；School of Automation, Beijing Institute of Technology（自动化学院，北京理工大学）
Efficient Navigation Among Movable Obstacles using a Mobile Manipulator via Hierarchical Policy Learning
School of Computing in Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院计算机学院）；Robotics Program at the same institute, KAIST（韩国科学技术院机器人项目）
Tsinghua University（清华大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））
In this paper, we propose a navigation algorithm oriented to multi-agent environment. This algorithm is expressed as a hierarchical framework that contains a Hidden Markov Model (HMM) and a Deep Reinforcement Learning (DRL) structure. For simplification, we term our method Hierarchical Navigation Reinforcement Network (HNRN). In high- level architecture, we train an HMM to evaluate the agent's perception to obtain a score. According to this score, adaptive control action will be chosen. While in low-level architecture, two sub-systems are introduced, one is a differential target- driven system…
University of Twente（特文特大学）；Jönköping University（延雪平大学）
Inspection and maintenance are two crucial aspects of industrial pipeline plants. While robotics has made tremendous progress in the mechanic design of in-pipe inspection robots, the autonomous control of such robots is still a big open challenge due to the high number of actuators and the complex manoeuvres required. To address this problem, we investigate the usage of Deep Reinforcement Learning for achieving autonomous navigation of in-pipe robots in pipeline networks with complex topologies. Moreover, we introduce a hierarchical policy decomposition based on Hierarchical Reinforcement Lear…
CL-CoTNav: Closed-Loop Hierarchical Chain-of-Thought for Zero-Shot Object-Goal Navigation with Vision-Language Models
School of Mechanical and Aerospace Engineering, Nanyang Technological University（南洋理工大学机械与宇航工程学院）；Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (ASTAR)（新加坡科技研究局信息通信研究院）；School of Science and Engineering, the Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）理工学院）；Shanghai AI Laboratory（上海人工智能实验室）
Hierarchical Decision-Making for Autonomous Navigation: Integrating Deep Reinforcement Learning and Fuzzy Logic in Four-Wheel Independent Steering and Driving Systems
School of Automation, Central South University（自动化学院，中南大学）
Representations are crucial for a robot to learn effective navigation policies. Recent work has shown that mid-level perceptual abstractions, such as depth estimates or 2D semantic segmentation, lead to more effective policies when provided as observations in place of raw sensor data (e.g., RGB images). However, such policies must still learn latent three-dimensional scene properties from mid-level abstractions. In contrast, high-level, hierarchical representations such as 3D scene graphs explicitly provide a scene's geometry, topology, and semantics, making them compelling representations for…
Hierarchical Semantic-Augmented Navigation: Optimal Transport and Graph-Driven Reasoning for Vision-Language Navigation
School of Software Engineering, Huazhong University of Science and Technology（华中科技大学软件学院）；Interdisciplinary Graduate Programme, Nanyang Technological University, Singapore（新加坡南洋理工大学交叉学科研究生项目）；University College London（伦敦大学学院）
Direct search for objects as part of navigation poses a challenge for small items. Utilizing context in the form of object-object relationships enable hierarchical search for targets efficiently. Most of the current approaches tend to directly incorporate sensory input into a reward-based learning approach, without learning about object relationships in the natural environment, and thus generalize poorly across domains. We present Memory-utilized Joint hierarchical Object Learning for Navigation in Indoor Rooms (MJOLNIR), a target-driven navigation algorithm, which considers the inherent relat…
School of Electrical and Computer Engineering, Georgia Institute of Technology（佐治亚理工学院电气与计算机工程学院）；Institute for Robotics and Intelligent Machines, Georgia Institute of Technology（佐治亚理工学院机器人与智能机器研究所）
This paper considers the integration of gap-based local navigation methods with artificial potential field (APF) methods to derive a local planning module for hierarchical navigation systems that has provable collision-free properties. Given that APF theory applies to idealized robot models, the provable properties are lost when applied to more realistic models. We describe a set of algorithm modifications that correct for these errors and enhance robustness to non-ideal models. Central to the construction of the local planner is the use of sensory-derived local free-space models that detect g…
HRO: Hierarchical Room-to-Object Framework for Zero-Shot Object Goal Navigation with Large Language Models
School of Computer Science and Technology, Xinjiang University（新疆大学计算机科学与技术学院）；Joint Research Laboratory for Embodied Intelligence, Xinjiang University（新疆大学具身智能联合研究实验室）；Joint International Research Laboratory of Silk Road Multilingual Cognitive Computing, Xinjiang University（新疆大学丝绸之路多语言认知计算国际联合研究实验室）
Most common navigation tasks in human environments require auxiliary arm interactions, e.g. opening doors, pressing buttons and pushing obstacles away. This type of navigation tasks, which we call Interactive Navigation, requires the use of mobile manipulators: mobile bases with manipulation capabilities. Interactive Navigation tasks are usually long-horizon and composed of heterogeneous phases of pure navigation, pure manipulation, and their combination. Using the wrong part of the embodiment is inefficient and hinders progress. We propose HRL4IN, a novel Hierarchical RL architecture for Inte…
CLASH: Collaborative Large-Small Hierarchical Framework for Continuous Vision-and-Language Navigation
Tongji University（同济大学）；Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology（能源化工过程智能制造重点实验室，教育部，东华大学）
A Deployable Embodied Vision-Language Navigation System with Hierarchical Cognition and Context-Aware Exploration
Center for Advanced Robotics Technology Innovation (CARTIN), School of Electrical and Electronic Engineering, Nanyang Technological University（先进机器人技术与创新中心（CARTIN）、电子工程学院、南洋理工大学）；Spatial AI & Robotics Lab, Department of Computer Science and Engineering（空间人工智能与机器人实验室、计算机科学与工程系）
Hierarchical RL-Guided Large-scale Navigation of a Snake Robot
Khoury College of Computer Science, Northeastern University（东北大学Khoury计算机科学学院）；SiliconSynapse Laboratory, Department of Electrical and Computer Engineering, Northeastern University（东北大学电气与计算机工程系硅突触实验室）
