External Human-Machine Interfaces (eHMI) are widely used on robots and autonomous vehicles to convey the machine's intent to humans. Delivery robots are getting common, and they share the sidewalk along with the pedestrians. Current research has explored the design of eHMI and its effectiveness for social robots and autonomous vehicles, but the use of eHMIs on delivery robots still remains unexplored. There is a knowledge gap on the effective use of eHMIs on delivery robots for indicating the robot's navigational intent to the pedestrians. An online survey with 152 participants was conducted to investigate the comprehensibility of the display and light-based eHMIs that convey the delivery robot's navigational intent under common navigation scenarios. Results show that display is preferred over lights in conveying the intent. The preferred type of content to be displayed varies according to the scenarios. Additionally, light is preferred as an auxiliary eHMI to present redundant information. The findings of this study can contribute to the development of future designs of eHMI on delivery robots.
University of Hamburg（汉堡大学）；SoftBank Robotics Europe（软银机器人欧洲公司）
Service robots need to show appropriate social behaviour in order to be deployed in social environments such as healthcare, education, retail, etc. Some of the main capabilities that robots should have are navigation and conversational skills. If the person is impatient, the person might want a robot to navigate faster and vice versa. Linguistic features that indicate politeness can provide social cues about a person's patient and impatient behaviour. The novelty presented in this paper is to dynamically incorporate politeness in robotic dialogue systems for navigation. Understanding the polit…
We present EWareNet, a novel intent and affect-aware social robot navigation algorithm among pedestrians. Our approach predicts the trajectory-based pedestrian intent from gait sequence, which is then used for intent-guided navigation taking into account social and proxemic constraints. We propose a transformer-based model that works on commodity RGB-D cameras mounted onto a moving robot. Our intent prediction routine is integrated into a mapless navigation scheme and makes no assumptions about the environment of pedestrian motion. Our navigation scheme consists of a novel obstacle profile rep…
IntentReact: Guiding Reactive Object-Centric Navigation via Topological Intent
School of Information Science and Engineering, Hangzhou Normal University（杭州师范大学信息科学与工程学院）；State Key Laboratory of Industrial Control and Technology, Zhejiang University（浙江大学工业控制技术国家重点实验室）；Neuromorphic Computing and Robotic Cognition Lab, Zhejiang University（浙江大学神经形态计算与机器人认知实验室）；Department of Automation, Zhejiang University of Technology（浙江工业大学自动化系）
Utilizing Vision-Language Models as Action Models for Intent Recognition and Assistance
University of Birmingham（伯明翰大学）；Queen Mary University of London（伦敦大学女王学院）；Aston University（阿斯顿大学）；United Kingdom National Nuclear Laboratory Ltd.（英国国家核实验室有限公司）
LogisticsVLN: Vision-Language Navigation For Low-Altitude Terminal Delivery Based on Agentic UAVs
School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所多模态人工智能系统国家重点实验室）；Department of Engineering Science, Faculty of Innovation Engineering, Macau University of Science and Technology（澳门科技大学创新工程学院工程科学系）；China Ship Research and Development Academy（中国船舶科研 Academy）；Obuda University（奥布达大学）；State Key Laboratory for Management and Control of Complex Systems, Chinese Academy of Sciences（中国科学院复杂系统管理与控制国家重点实验室）
Learning Robot Visual Navigation in Crowds via Intention-Aware Scene Representations
Shenzhen Key Laboratory of Robotics Perception and Intelligence, Department of Electronic and Electrical Engineering, SUSTech（南方科技大学电子与电气工程系深圳市机器人感知与智能重点实验室）；Jiaxing Research Institute, SUSTech（南方科技大学嘉兴研究院）；Research Institute of MA&EI, Peng Cheng Laboratory（鹏城实验室机器人与人工智能研究所）
IntentNav: Learning Spatial-Visual Object Navigation from Human Demonstrations
Nanyang Technological University（南洋理工大学）；Carnegie Mellon University（卡内基梅隆大学）；The Chinese University of Hong Kong（香港中文大学）；A*STAR Institute for Infocomm Research (I2R)（新加坡科技研究局资讯通信研究院）
OpenBench: A New Benchmark and Baseline for Semantic Navigation in Smart Logistics
Institute for AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院）；College of Information Science and Technology, Beijing University of Chemical Technology（北京化工大学信息科学与技术学院）；School of Astronautics, Harbin Institute of Technology（哈尔滨工业大学航天学院）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；School of Mechanical and Vehicular Engineering, Beijing Institute of Technology（北京理工大学机械与车辆学院）；School of Vehicle and Mobility, Tsinghua University（清华大学车辆与 mobility学院）
University of Birmingham（伯明翰大学）；University of West Attica（西阿提卡大学）
This paper addresses the problem of human operator intent recognition during teleoperated robot navigation. In this context, recognition of the operator's intended navigational goal, could enable an artificial intelligence (AI) agent to assist the operator in an advanced human-robot interaction framework. We propose a Bayesian Operator Intent Recognition (BOIR) probabilistic method that utilizes: (i) an observation model that fuses information as a weighting combination of multiple observation sources providing geometric information; (ii) a transition model that indicates the evolution of the …
People are proficient at communicating their intentions in order to avoid conflicts when navigating in narrow, crowded environments. In many situations mobile robots lack both the ability to interpret human intentions and the ability to clearly communicate their own intentions to people sharing their space. This work addresses the second of these points, leveraging insights about how people implicitly communicate with each other through observations of behaviors such as gaze to provide mobile robots with better social navigation skills. In a preliminary human study, the importance of gaze as a…
Universidade Federal de Minas Gerais（米纳斯吉拉斯联邦大学）；Instituto Tecnológico Vale (ITV)（淡水河谷技术研究所）
The use of delivery services is an increasing trend worldwide, further enhanced by the COVID pandemic. In this context, drone delivery systems are of great interest as they may allow for faster and cheaper deliveries. This paper presents a navigation system that makes feasible the delivery of parcels with autonomous drones. The system generates a path between a start and a final point and controls the drone to follow this path based on its localization obtained through GPS, 9DoF IMU, and barometer. In the landing phase, information of poses estimated by a marker (ArUco) detection technique usi…
Tactile-Based Human Intent Recognition for Robot Assistive Navigation
Electrical & Computer Engineering, University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校电气与计算机工程系）；Siebel School of Computing and Data Science, University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校Siebel计算与数据科学学院）
Robert Bosch Center for Cyber-Physical Systems（罗伯特·博世信息物理系统研究中心）；Department of Aerospace Engineering（航空航天工程系）；Indian Institute of Science（印度科学学院）
Aerial package transportation often requires complex spatial and temporal specifications to be satisfied in order to ensure safe and timely delivery from one point to another. It is usually efficient to transport versatile payloads using multiple UAVs that can work collaboratively to achieve the desired task. The complex temporal specifications can be handled coherently by applying Signal Temporal Logic (STL) to dynamical systems. This paper addresses the problem of waypoint navigation of a multi-UAV payload system under temporal specifications using higher-order time-varying control barrier f…
We propose a distributed planning method with asynchronous execution for multi-agent pickup and delivery (MAPD) problems for environments with occasional delays in agents' activities and flexible endpoints. MAPD is a crucial problem framework with many applications; however, most existing studies assume ideal agent behaviors and environments, such as a fixed speed of agents, synchronized movements, and a well-designed environment with many short detours for multiple agents to perform tasks easily. However, such an environment is often infeasible; for example, the moving speed of agents may be …
Most current approaches to social navigation focus on the trajectory and position of participants in the interaction. Our current work on the topic focuses on integrating gaze into social navigation, both to cue nearby pedestrians as to the intended trajectory of the robot and to enable the robot to read the intentions of nearby pedestrians. This paper documents a series of experiments in our laboratory investigating the role of gaze in social navigation.
The combination of data from multiple sensors, also known as sensor fusion or data fusion, is a key aspect in the design of autonomous robots. In particular, algorithms able to accommodate sensor fusion techniques enable increased accuracy, and are more resilient against the malfunction of individual sensors. The development of algorithms for autonomous navigation, mapping and localization have seen big advancements over the past two decades. Nonetheless, challenges remain in developing robust solutions for accurate localization in dense urban environments, where the so called last-mile delive…
As autonomous driving technology is getting more and more mature today, autonomous delivery companies like Starship, Marble, and Nuro has been making progress in the tests of their autonomous delivery robots. While simulations and simulators are very important for the final product landing of the autonomous delivery robots since the autonomous delivery robots need to navigate on the sidewalk, campus, and other urban scenarios, where the simulations can avoid real damage to pedestrians and properties in the real world caused by any algorithm failures and programming errors and thus accelerate t…
University of Edinburgh（爱丁堡大学）；Sapienza University of Rome（罗马大学）
Many modern robotics applications require robots to function autonomously in dynamic environments including other decision making agents, such as people or other robots. This calls for fast and scalable interactive motion planning. This requires models that take into consideration the other agent's intended actions in one's own planning. We present a real-time motion planning framework that brings together a few key components including intention inference by reasoning counterfactually about potential motion of the other agents as they work towards different goals. By using a light-weight moti…
MRReP: Mixed Reality-based Hand-drawn Reference Path Editing Interface for Mobile Robot Navigation
Graduate School of Information Science and Technology, The University of Osaka（大阪大学信息科学与技术研究生院）；D3 Center, The University of Osaka（大阪大学D3中心）；Graduate School of Maritime Sciences, Kobe University（神户大学海事科学研究生院）
Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；ETH Zurich（苏黎世联邦理工学院）；Max Planck and ETH Center for Learning Systems（马克斯·普朗克-苏黎世联邦理工学院学习系统中心）；Koç University（科奇大学）
Magnetic resonance imaging (MRI)-guided robotic systems offer great potential for new minimally invasive medical tools, including MRI-powered miniature robots. By re-purposing the imaging hardware of an MRI scanner, the magnetic miniature robot could be navigated into the remote part of the patient's body without needing tethered endoscopic tools. However, the state-of-art MRI-powered magnetic miniature robots have limited functionality besides navigation. Here, we propose an MRI-powered magnetic miniature capsule robot benefiting from acoustic streaming forces generated by MRI-guided high-int…
Narrate2Nav: Real-Time Visual Navigation with Implicit Language Reasoning in Human-Centric Environments
Department of Computer Science, George Mason University（计算机科学系，乔治·马歇尔大学）；Department of Information Sciences and Technology, George Mason University（信息科学与技术系，乔治·马歇尔大学）
UAS Technikum Wien（维也纳技术高等学院）；Ural Federal University（乌拉尔联邦大学）；Johannes Kepler University Linz（林茨约翰内斯开普勒大学）
In the context of Intelligent Transportation Systems and the delivery of goods, new technology approaches need to be developed in order to cope with certain challenges that last mile delivery entails, such as navigation in an urban environment. Autonomous delivery robots can help overcome these challenges. We propose a method for performing mixed reality (MR) simulation with ROS-based robots using Unity, which synchronizes the real and virtual environment, and simultaneously uses the sensor information of the real robots to locate themselves and project them into the virtual environment, so th…
Mobile robots have gained increased importance within industrial tasks such as commissioning, delivery or operation in hazardous environments. The ability to autonomously navigate safely especially within dynamic environments, is paramount in industrial mobile robotics. Current navigation methods depend on preexisting static maps and are error-prone in dynamic environments. Furthermore, for safety reasons, they often rely on hand-crafted safety guidelines, which makes the system less flexible and slow. Visual based navigation and high level semantics bear the potential to enhance the safety of…
A Survey on Socially Aware Robot Navigation: Taxonomy and Future Challenges
LAAS-CNRS, Universite de Toulouse（图卢兹大学LAAS-CNRS）；RoboLab, Universidad de Extremadura（埃斯特雷马杜拉大学RoboLab）；Aston University（阿斯顿大学）；Universitat Politècnica de Catalunya（加泰罗尼亚理工大学）；Universite Grenoble Alpes, Inria（格勒诺布尔阿尔卑斯大学Inria）
Zhejiang University（浙江大学）；The University of Technology, Sydney（悉尼科技大学）
Autonomous navigation based on precise localization has been widely developed in both academic research and practical applications. The high demand for localization accuracy has been essential for safe robot planing and navigation while it makes the current geometric solutions less robust to environmental changes. Recent research on end-to-end methods handle raw sensory data with forms of navigation instructions and directly output the command for robot control. However, the lack of intermediate semantics makes the system more rigid and unstable for practical use. To explore these issues, this…
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…
Clinically Ready Magnetic Microrobots for Targeted Therapies
Magnebotix AG（Magnebotix公司）；University of Würzburg（维尔茨堡大学）；University of Thessaly（色萨利大学）；University of Barcelona（巴塞罗那大学）；Institució Catalana de Recerca i Estudis Avançats (ICREA)（加泰罗尼亚研究与高等教育学院）；Porto University（波尔图大学）；Fudan University（复旦大学）；Yiwu Research Institute of Fudan University（复旦大学义乌研究院）；University Hospital of Zurich（苏黎世大学医院）；University of Zurich（苏黎世大学）；University Children's Hospital Zurich（苏黎世大学儿童医院）；Kantonsspital Aarau AG（阿尔高州立医院）；University Hospital Zurich（苏黎世大学医院）
Robots that can effectively understand human intentions from actions are crucial for successful human-robot collaboration. In this work, we address the challenge of a robot navigating towards an unknown goal while also accounting for a human's preference for a particular path in the presence of obstacles. This problem is particularly challenging when both the goal and path preference are unknown a priori. To overcome this challenge, we propose a method for encoding and inferring path preference online using a partitioning of the space into polytopes. Our approach enables joint inference over t…
In this paper, we present a planning framework that uses a combination of implicit (robot motion) and explicit (visual/audio/haptic feedback) communication during mobile robot navigation. First, we developed a model that approximates both continuous movements and discrete behavior modes in human navigation, considering the effects of implicit and explicit communication on human decision making. The model approximates the human as an optimal agent, with a reward function obtained through inverse reinforcement learning. Second, a planner uses this model to generate communicative actions that max…
