Seamless loco-manipulation in unstructured environments requires robots to leverage autonomous exploration alongside whole-body control for physical interaction. In this work, we introduce HANDO (Hierarchical Autonomous Navigation and Dexterous Omni-loco-manipulation), a two-layer framework designed for legged robots equipped with manipulators to perform human-centered mobile manipulation tasks. The first layer utilizes a goal-conditioned autonomous exploration policy to guide the robot to semantically specified targets, such as a black office chair in a dynamic environment. The second layer employs a unified whole-body loco-manipulation policy to coordinate the arm and legs for precise interaction tasks-for example, handing a drink to a person seated on the chair. We have conducted an initial deployment of the navigation module, and will continue to pursue finer-grained deployment of whole-body loco-manipulation.

The increasing demand for efficient last-mile delivery in smart logistics underscores the role of autonomous robots in enhancing operational efficiency and reducing costs. Traditional navigation methods, which depend on high-precision maps, are resource-intensive, while learning-based approaches often struggle with generalization in real-world scenarios. To address these challenges, this work proposes the Openstreetmap-enhanced oPen-air sEmantic Navigation (OPEN) system that combines foundation models with classic algorithms for scalable outdoor navigation. The system uses off-the-shelf OpenStreetMap (OSM) for flexible map representation, thereby eliminating the need for extensive pre-mapping efforts. It also employs Large Language Models (LLMs) to comprehend delivery instructions and Vision-Language Models (VLMs) for global localization, map updates, and house number recognition. To compensate the limitations of existing benchmarks that are inadequate for assessing last-mile delivery, this work introduces a new benchmark specifically designed for outdoor navigation in residential areas, reflecting the real-world challenges faced by autonomous delivery systems. Extensive experiments in simulated and real-world environments demonstrate the proposed system's efficacy in enhancing navigation efficiency and reliability. To facilitate further research, our code and benchmark are publicly available.

Real-world last-mile delivery datasets are crucial for research in logistics, supply chain management, and spatio-temporal data mining. Despite a plethora of algorithms developed to date, no widely accepted, publicly available last-mile delivery dataset exists to support research in this field. In this paper, we introduce \texttt{LaDe}, the first publicly available last-mile delivery dataset with millions of packages from the industry. LaDe has three unique characteristics: (1) Large-scale. It involves 10,677k packages of 21k couriers over 6 months of real-world operation. (2) Comprehensive information. It offers original package information, such as its location and time requirements, as well as task-event information, which records when and where the courier is while events such as task-accept and task-finish events happen. (3) Diversity. The dataset includes data from various scenarios, including package pick-up and delivery, and from multiple cities, each with its unique spatio-temporal patterns due to their distinct characteristics such as populations. We verify LaDe on three tasks by running several classical baseline models per task. We believe that the large-scale, comprehensive, diverse feature of LaDe can offer unparalleled opportunities to researchers in the supply chain community, data mining community, and beyond. The dataset homepage is publicly available at https://huggingface.co/datasets/Cainiao-AI/LaDe.

Within the domain of e-commerce retail, an important objective is the reduction of parcel loss during the last-mile delivery phase. The ever-increasing availability of data, including product, customer, and order information, has made it possible for the application of machine learning in parcel loss prediction. However, a significant challenge arises from the inherent imbalance in the data, i.e., only a very low percentage of parcels are lost. In this paper, we propose two machine learning approaches, namely, Data Balance with Supervised Learning (DBSL) and Deep Hybrid Ensemble Learning (DHEL), to accurately predict parcel loss. The practical implication of such predictions is their value in aiding e-commerce retailers in optimizing insurance-related decision-making policies. We conduct a comprehensive evaluation of the proposed machine learning models using one year data from Belgian shipments. The findings show that the DHEL model, which combines a feed-forward autoencoder with a random forest, achieves the highest classification performance. Furthermore, we use the techniques from Explainable AI (XAI) to illustrate how prediction models can be used in enhancing business processes and augmenting the overall value proposition for e-commerce retailers in the last mile delivery.

Hyundai Motor Group (the Group) has started two pilot delivery service programs using autonomous robots based on its Plug & Drive (PnD) modular platform at a hotel and a residential-commercial complex located in the outskirts of Seoul. The delivery robot consists of a storage unit integrated on top of a PnD driving unit. Alongside the loading box used to deliver items, a connected screen displays information for customers. First shown at CES 2022, the Group's PnD modular platform is an all-in-one single wheel unit that combines intelligent steering, braking, in-wheel electric drive and suspension hardware, including a steering actuator for 360-degree, holonomic rotation. It moves autonomously with the aid of LiDAR and camera sensors.

This paper investigates the end-user acceptance of last-mile delivery carried out by autonomous vehicles within the United States. A total of 296 participants were presented with information on this technology and then asked to complete a questionnaire on their perceptions to gauge their behavioral intention concerning acceptance. Structural equation modeling of the partial least squares flavor (PLS-SEM) was employed to analyze the collected data. The results indicated that the perceived usefulness of the technology played the greatest role in end-user acceptance decisions, followed by the influence of others, and then the enjoyment received by interacting with the technology. Furthermore, the perception of risk associated with using autonomous delivery vehicles for last-mile delivery led to a decrease in acceptance. However, most participants did not perceive the use of this technology to be risky. The paper concludes by summarizing the implications our findings have on the respective stakeholders, and proposing the next steps in this area of research.

Artificial Intelligence in Logistics involves using technology to automate complex tasks and unearthing previously unknown patterns in Supply chain processes or workflows, and the impact is game-changing and visible. AI systems also enable predictive analytics, which helps tackle operational challenges and disruptions to supply chains as well as the workforce. A constant challenge with manufacturing is the losses from overstocking or under-stocking inventories. Overstocking often leads to wastage and lower margins. Under-stocking can translate into losses in sales, revenue, and customers.

Luis Vera believes the third time is the charm. The self-proclaimed serial entrepreneur admits that his vision for digitizing retail was a decade or two early when he started his journey in the 90s. Through a pair of startups -- Prospect and SCOPIX -- he tried a variety of methods to help capture store inventory, including placings cameras on shelves and a ceiling-based system where one ran on tracks. He was, effectively, attempting to compete with Amazon well before Amazon was, well, Amazon -- at least in any meaningful sense. Computer vision, machine learning and the like have caught up a lot since then, of course.

To make ALMDVs a daily reality, the first step is legislation. There are various ways to categorize AVs: people-carriers or goods-carriers; operating on public roads or private property; high speed or low speed, and so on. But which type of regulations should be apply to the Autonomous Last Mile Delivery Vehicle (ALMDV)? Is it a vehicle, a non-motor vehicle, a personal delivery device, or a robot? The answer to this question ultimately determines which lane an ALMDV will be allowed to drive.

To begin, using machine learning in supply chain management may aid in the automation of a variety of routine operations, allowing businesses to focus on more strategic and significant business activities. Supply chain managers may use sophisticated machine learning tools to optimize inventories and locate the best suppliers to keep their business operating smoothly. ML has piqued the interest of a growing number of organizations, owing to its numerous benefits, including the ability to fully leverage the massive volumes of data generated by warehousing, transportation systems, and industrial logistics. It may also assist businesses in developing a complete machine intelligence-powered supply chain model to reduce risks, increase insights, and improve performance, all of which are critical components of a globally competitive supply chain. Machine learning has a lot of applications in the supply chain because it is such a data-driven business.