Mashable @Home Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Look Up Trending Now Mashable Selects Say More Top creators, ranked Gift Ideas For Everyone On Your List Creator Playbook In My Bag AI at School Safety Net All Series Leah Stodart is a Philadelphia-based Senior Shopping Reporter at Mashable where she covers and tests essential home tech like vacuums, TVs, beauty devices, and eco-friendly hacks. Her ever-evolving experience in these categories helps her make thoughtful recommendations for how to spend your money during shopping holidays like Black Friday, which Leah has been covering for Mashable since 2017. All products featured here are independently selected by our editors and writers. If you buy something through links on our site, Mashable may earn an affiliate commission. There's definitely something to be said for the crisp handheld control of a good cordless stick vacuum . But if you don't consider cleaning cathartic, the best robot vacuums can cross that chore off your to-do list for good. And after testing more than 40 robot vacuums on my own personal floors (my apartment is clean), I can confidently curate a handful of robot vacuum recommendations that'll make your life easier -- and fit your budget. Here are the best robot vacuums to buy in 2026. SEE ALSO: 'Why does my robot vacuum suck now?' Well, when's the last time you cleaned it? I test new robot vacuums on a regular basis and compare them to the top performers from previous months, with all my evaluations taking place in my own home on various rugs, tile, and hardwood floors .

Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Look Up Say More Creator Hub Gift Ideas For Everyone On Your List Mashable Selects Versus Switch Off Trending Now Safety Net In My Bag VidCon with Mashable All Series I give robot vacuum advice to friends and family all the time. Leah Stodart is a Philadelphia-based Senior Shopping Reporter at Mashable where she covers and tests essential home tech like vacuums, TVs, beauty devices, and eco-friendly hacks. Her ever-evolving experience in these categories helps her make thoughtful recommendations for how to spend your money during shopping holidays like Black Friday, which Leah has been covering for Mashable since 2017. All products featured here are independently selected by our editors and writers. If you buy something through links on our site, Mashable may earn an affiliate commission. Recommendations for the best robot vacuums to buy in 2026 are at the bottom of this guide. Use the table of contents to skip down to that, or to skip to the deep dive on a specific robot vacuum feature.

Unmanned aerial vehicles could accurately accomplish complex navigation and obstacle avoidance tasks under external control. However, enabling unmanned aerial vehicles (UAVs) to rely solely on onboard computation and sensing for real-time navigation and dynamic obstacle avoidance remains a significant challenge due to stringent latency and energy constraints. Inspired by the efficiency of biological systems, we propose a fully neuromorphic framework achieving end-to-end obstacle avoidance during navigation with an overall latency of just 2.3 milliseconds. Specifically, our bio-inspired approach enables accurate moving object detection and avoidance without requiring target recognition or trajectory computation. Additionally, we introduce the first monocular event-based pose correction dataset with over 50,000 paired and labeled event streams.

When you purchase through links in our articles, we may earn a small commission. The Dyson Spot+Scrub Ai is a polished, high-end robot vacuum/mop best suited to homes with hard floors, pets, and everyday clutter. Its stain-cleaning system is clever, but not hands-off enough to fully justify the $1,199 price for buyers focused mainly on mopping. Robot mops are usually better at maintaining clean floors than tackling truly dirty ones. They can wipe away light daily grime and keep hard floors looking presentable, but sticky spills and dried-on stains still expose the limits of "hands-free" cleaning.

In what follows, we give some details of content omitted in the paper due to space limit. The supplements are organized as follows. We give some proof of Lemma 1, 2, Proposition 1, Lemma 3, 4, and Theorem 2 in Section A.1 -A.6, respectively. We provide some training details in Section A.13 as well as experiment details and results in Section A.14. We compare polynomial CBFs with NCBF in A.15, compare NCBFs with different activation functions in A.16. A.1 Proof of Lemma 1 We prove by induction on L. If L =1, then x 2 X(S) if the pre-activation input to the (1,j) neuron is nonnegative for all j 2 S1 and nonpositive for all j/2 S1. We have that the pre-activation input is equal to WT1jx+r1j, establishing the result for L =1 .

In CBF-based control, the desired safety properties of the system are mapped to nonnegativity of a CBF, and the control input is chosen to ensure that the CBF remains nonnegative for all time.

In what follows, we give some details of content omitted in the paper due to space limit. The proof approach is based on Nagumo's Theorem, which gives necessary and sufficient conditions Definition 2. Let A be a closed set. The following is a fundamental preliminary result for establishing positive invariance. Proposition 2. F or any x 2 @ D, we have T Lemma 2 is a consequence of Proposition 2. For ease of exposition, we first reproduce the lemma First, suppose that condition (i) holds. Next, suppose that condition (ii) holds.

Abstract-- We present a novel modularized end-to-end framework for legged reactive navigation in complex dynamic environments using a single light detection and ranging (LiDAR) sensor . The system comprises four simulation-trained modules: three reinforcement-learning (RL) policies for locomotion, safety shielding, and navigation, and a transformer-based exteroceptive estimator that processes raw point-cloud inputs. This modular decomposition of complex legged motor-control tasks enables lightweight neural networks with simple architectures, trained using standard RL practices with targeted reward shaping and curriculum design, without reliance on heuristics or sophisticated policy-switching mechanisms. We conduct comprehensive ablations to validate our design choices and demonstrate improved robustness compared to existing approaches in challenging navigation tasks. The resulting reactive safe navigation (REASAN) system achieves fully onboard and real-time reactive navigation across both single-and multi-robot settings in complex environments. We release our training and deployment code at https://github.com/ASIG-X/REASAN Legged robots offer distinct advantages given their universal mobility, with expanding application scenarios ranging over search and rescue, logistics, entertainment, industrial inspection, and forestry inventories [1]-[4]. Recent advances in quadrupedal locomotion have demonstrated remarkable performance, particularly, in handling complex static terrains [5]-[7].

The development of athletic humanoid robots has gained significant attention as advances in actuation, sensing, and control enable increasingly dynamic, real-world capabilities. RoboCup, an international competition of fully autonomous humanoid robots, provides a uniquely challenging benchmark for such systems, culminating in the long-term goal of competing against human soccer players by 2050. This paper presents the hardware and software innovations underlying our team's victory in the RoboCup 2024 Adult-Sized Humanoid Soccer Competition. On the hardware side, we introduce an adult-sized humanoid platform built with lightweight structural components, high-torque quasi-direct-drive actuators, and a specialized foot design that enables powerful in-gait kicks while preserving locomotion robustness. On the software side, we develop an integrated perception and localization framework that combines stereo vision, object detection, and landmark-based fusion to provide reliable estimates of the ball, goals, teammates, and opponents. A mid-level navigation stack then generates collision-aware, dynamically feasible trajectories, while a centralized behavior manager coordinates high-level decision making, role selection, and kick execution based on the evolving game state. The seamless integration of these subsystems results in fast, precise, and tactically effective gameplay, enabling robust performance under the dynamic and adversarial conditions of real matches. This paper presents the design principles, system architecture, and experimental results that contributed to ARTEMIS's success as the 2024 Adult-Sized Humanoid Soccer champion.