The best vacuums are lightweight with strong filtration systems and multiple attachments. Keeping your home spotless can sometimes feel like a never-ending battle, but with the right vacuum, you can actually make cleaning easy. Whether you're dealing with pet hair, constant dust and kids' messes, a powerful vacuum is your best ally. We've done a deep dive into the best vacuums on the market, from Dyson to Shark, Miele and other well-known brands. There are lightweight options, hands-free options and vacuums that are extra-tough on pet hair.

Warehouse robotic systems equipped with vacuum grippers must reliably grasp a diverse range of objects from densely packed shelves. However, these environments present significant challenges, including occlusions, diverse object orientations, stacked and obstructed items, and surfaces that are difficult to suction. We introduce \tetra, a novel vacuum-based grasping strategy featuring four suction cups mounted on linear actuators. Each actuator is equipped with an optical time-of-flight (ToF) proximity sensor, enabling reactive grasping. We evaluate \tetra in a warehouse-style setting, demonstrating its ability to manipulate objects in stacked and obstructed configurations. Our results show that our RL-based policy improves picking success in stacked-object scenarios by 22.86\% compared to a single-suction gripper. Additionally, we demonstrate that TetraGrip can successfully grasp objects in scenarios where a single-suction gripper fails due to physical limitations, specifically in two cases: (1) picking an object occluded by another object and (2) retrieving an object in a complex scenario. These findings highlight the advantages of multi-actuated, suction-based grasping in unstructured warehouse environments. The project website is available at: \href{https://tetragrip.github.io/}{https://tetragrip.github.io/}.

While recent advances in object suction grasping have shown remarkable progress, significant challenges persist particularly in cluttered and complex parcel handling scenarios. Two fundamental limitations hinder current approaches: (1) the lack of a comprehensive suction grasp dataset tailored for parcel manipulation tasks, and (2) insufficient adaptability to diverse object characteristics including size variations, geometric complexity, and textural diversity. To address these challenges, we present Parcel-Suction-Dataset, a large-scale synthetic dataset containing 25 thousand cluttered scenes with 410 million precision-annotated suction grasp poses. This dataset is generated through our novel geometric sampling algorithm that enables efficient generation of optimal suction grasps incorporating both physical constraints and material properties. We further propose Diffusion-Suction, an innovative framework that reformulates suction grasp prediction as a conditional generation task through denoising diffusion probabilistic models. Our method iteratively refines random noise into suction grasp score maps through visual-conditioned guidance from point cloud observations, effectively learning spatial point-wise affordances from our synthetic dataset. Extensive experiments demonstrate that the simple yet efficient Diffusion-Suction achieves new state-of-the-art performance compared to previous models on both Parcel-Suction-Dataset and the public SuctionNet-1Billion benchmark.

The irrigation-suction process is a common procedure to rinse and clean up the surgical field in minimally invasive surgery (MIS). In this process, surgeons first irrigate liquid, typically saline, into the surgical scene for rinsing and diluting the contaminant, and then suction the liquid out of the surgical field. While recent advances have shown promising results in the application of reinforcement learning (RL) for automating surgical subtasks, fewer studies have explored the automation of fluid-related tasks. In this work, we explore the automation of both steps in the irrigation-suction procedure and train two vision-based RL agents to complete irrigation and suction autonomously. To achieve this, a platform is developed for creating simulated surgical robot learning environments and for training agents, and two simulated learning environments are built for irrigation and suction with visually plausible fluid rendering capabilities. With techniques such as domain randomization (DR) and carefully designed reward functions, two agents are trained in the simulator and transferred to the real world. Individual evaluations of both agents show satisfactory real-world results. With an initial amount of around 5 grams of contaminants, the irrigation agent ultimately achieved an average of 2.21 grams remaining after a manual suction. As a comparison, fully manual operation by a human results in 1.90 grams remaining. The suction agent achieved 2.64 and 2.24 grams of liquid remaining across two trial groups with more than 20 and 30 grams of initial liquid in the container. Fully autonomous irrigation-suction trials reduce the contaminant in the container from around 5 grams to an average of 2.42 grams, although yielding a higher total weight remaining (4.40) due to residual liquid not suctioned. Further information about the project is available at https://tbs-ualberta.github.io/CRESSim/.

This paper addresses the challenges of vision-based manipulation for autonomous cutting and unpacking of transparent plastic bags in industrial setups, aligning with the Industry 4.0 paradigm. Industry 4.0, driven by data, connectivity, analytics, and robotics, promises enhanced accessibility and sustainability throughout the value chain. The integration of autonomous systems, including collaborative robots (cobots), into industrial processes is pivotal for efficiency and safety. The proposed solution employs advanced Machine Learning algorithms, particularly Convolutional Neural Networks (CNNs), to identify transparent plastic bags under varying lighting and background conditions. Tracking algorithms and depth sensing technologies are utilized for 3D spatial awareness during pick and placement. The system addresses challenges in grasping and manipulation, considering optimal points, compliance control with vacuum gripping technology, and real-time automation for safe interaction in dynamic environments. The system's successful testing and validation in the lab with the FRANKA robot arm, showcases its potential for widespread industrial applications, while demonstrating effectiveness in automating the unpacking and cutting of transparent plastic bags for an 8-stack bulk-loader based on specific requirements and rigorous testing.

Robot vacuums hold an undeniable allure: the promise to handle your floor cleaning while you kick back and relax. While premium models can cost well over 1,000, budget-friendly options starting under 200 make automated cleaning seem within reach for almost any budget. But are these wallet-friendly bots actually worth it, or are you better off saving up for a premium model? Read on for my advice, and be sure to browse our top robot vacuum and mop picks once you're ready to shop. Lower-cost robot vacuums will deliver basic cleaning capabilities, but your user experience may leave something to be desired.

If you want your home cleaned by a robot that leaves you as little work as possible, Wellbots has a deal for you. The iRobot Roomba Combo 10 Max Robot AutoWash Dock has an unwieldy name but a robust feature set. You can take 425 off the robovac with coupon code ENGBF425. Unveiled this summer, the (deep breath) Roomba Combo 10 Max Robot AutoWash Dock is iRobot's most advanced (and expensive) robot vacuum to date. Although its 1,399 MSRP prices it out of most homes, this deal makes it more reasonable for folks who don't mind splurging for a cutting-edge cleaner that lets you spend your time doing something fun.

Only six months after iRobot launched its budget-friendly Essential robot vacuums, the lineup already has two new models. The Roomba Combo 2 Essential and Roomba Vac 2 Essential "double the cleaning power" of their predecessors and bring self-emptying dust bins to the series for the first time. The Roomba Combo 2 Essential ( 425) combines vacuuming and mopping capabilities, while the Vac 2 Essential ( 400) is a vacuuming-only model for those without tile or hardwood floors. Both have 100 percent stronger suction than the first Essential vacs. The latter already had 20 times the suction power of the Roomba 600 series it replaced on the low end of iRobot's product line.

We may earn revenue from the products available on this page and participate in affiliate programs. Pet owners know keeping fur from collecting in corners, couch crevices, and our clothes is a Sisyphean task, but a robot vacuum for pet hair can help. It doesn't matter if scritch sessions end in a handful of Himalayan or husky; our fate is to vacuum again and again, forever. But that kind of menial, repetitive task can easily be outsourced to a robot vacuum that will never tire--even during seasonal shedding. Since their debut, robot vacuums have gotten smarter, stronger, and added a bunch of bells and whistles like scheduling, hands-free emptying, and mops so you can delegate more cleaning tasks (all featured in our best overall pick, the ECOVACS Deebot T30S Combo). Here's our guide to finding the best robot vacuums for pet hair so that you can spend more time with your furry friend(s) and less worrying about the floors.

The rise of Large Language Models (LLMs) has impacted research in robotics and automation. While progress has been made in integrating LLMs into general robotics tasks, a noticeable void persists in their adoption in more specific domains such as surgery, where critical factors such as reasoning, explainability, and safety are paramount. Achieving autonomy in robotic surgery, which entails the ability to reason and adapt to changes in the environment, remains a significant challenge. In this work, we propose a multi-modal LLM integration in robot-assisted surgery for autonomous blood suction. The reasoning and prioritization are delegated to the higher-level task-planning LLM, and the motion planning and execution are handled by the lower-level deep reinforcement learning model, creating a distributed agency between the two components. As surgical operations are highly dynamic and may encounter unforeseen circumstances, blood clots and active bleeding were introduced to influence decision-making. Results showed that using a multi-modal LLM as a higher-level reasoning unit can account for these surgical complexities to achieve a level of reasoning previously unattainable in robot-assisted surgeries. These findings demonstrate the potential of multi-modal LLMs to significantly enhance contextual understanding and decision-making in robotic-assisted surgeries, marking a step toward autonomous surgical systems.