Plants need regular and the appropriate amount of watering to thrive and survive. While agricultural robots exist that can spray water on plants and crops such as the , they are expensive and have limited mobility and/or functionality. We introduce a novel autonomous mobile plant watering robot that uses a 6 degree of freedom (DOF) manipulator, connected to a 4 wheel drive alloy chassis, to be able to hold a garden hose, recognize and detect plants, and to water them with the appropriate amount of water by being able to insert a soil humidity/moisture sensor into the soil. The robot uses Jetson Nano and Arduino microcontroller and real sense camera to perform computer vision to detect plants using real-time YOLOv5 with the Pl@ntNet-300K dataset. The robot uses LIDAR for object and collision avoideance and does not need to move on a pre-defined path and can keep track of which plants it has watered. We provide the Denavit-Hartenberg (DH) Table, forward kinematics, differential driving kinematics, and inverse kinematics along with simulation and experiment results

Wybot's solar skimmer does a surprisingly good job of grabbing leaves off the surface of the pool, but its loud operation and poor power management knock it down a peg. Solar-powered pool skimmers flit along the surface of your pool operating under the idea that if they can scoop up debris before it sinks, you won't need to clean the bottom of the pool. It sounds logical, but in practice, most pool skimmers don't do the absolute best of jobs--there's only so much surface area a skimmer can cover before leaves get waterlogged and sink to the depths. But robotic skimmers are better than nothing, especially if you don't have a good in-wall skimmer. The Wybot F1 Pool Skimmer was much more effective at capturing floating leaves than any skimmer I've used to date.

The competition over natural reserves of minerals is expected to increase in part because of the linear-economy paradigm based on take-make-dispose. Simultaneously, the linear economy considers end-of-use products as waste rather than as a resource, which results in large volumes of waste whose management remains an unsolved problem. Since a transition to a circular economy can mitigate these open issues, in this paper we begin by enhancing the notion of circularity based on compartmental dynamical thermodynamics, namely, $λ$, and then, we model a thermodynamical material network processing a batch of 2 solid materials of criticality coefficients of 0.1 and 0.95, with a robotic disassembler compartment controlled via reinforcement learning (RL), and processing 2-7 kg of materials. Subsequently, we focused on the design of the robotic disassembler compartment using state-of-the-art RL algorithms and assessing the algorithm performance with respect to $λ$ (Fig. 1). The highest circularity is -2.1 achieved in the case of disassembling 2 parts of 1 kg each, whereas it reduces to -7.2 in the case of disassembling 4 parts of 1 kg each contained inside a chassis of 3 kg. Finally, a sensitivity analysis highlighted that the impact on $λ$ of the performance of an RL controller has a positive correlation with the quantity and the criticality of the materials to be disassembled. This work also gives the principles of the emerging research fields indicated as circular intelligence and robotics (CIRO). Source code is publicly available.

This paper describes the development of an autonomous car by the UruBots team for the 2025 FIRA Autonomous Cars Challenge (Pro). The project involves constructing a compact electric vehicle, approximately the size of an RC car, capable of autonomous navigation through different tracks. The design incorporates mechanical and electronic components and machine learning algorithms that enable the vehicle to make real-time navigation decisions based on visual input from a camera. We use deep learning models to process camera images and control vehicle movements. Using a dataset of over ten thousand images, we trained a Convolutional Neural Network (CNN) to drive the vehicle effectively, through two outputs, steering and throttle. The car completed the track in under 30 seconds, achieving a pace of approximately 0.4 meters per second while avoiding obstacles.

-- Learning-based methods, such as imitation learning (IL) and reinforcement learning (RL), can produce excel control policies over challenging agile robot tasks, such as sports robot. However, no existing work has harmonized learning-based policy with model-based methods to reduce training complexity and ensure the safety and stability for agile badminton robot control. In this paper, we introduce Hamlet, a novel hybrid control system for agile badminton robots. Specifically, we propose a model-based strategy for chassis locomotion which provides a base for arm policy. We introduce a physics-informed "IL+RL " training framework for learning-based arm policy. In this train framework, a model-based strategy with privileged information is used to guide arm policy training during both IL and RL phases. In addition, we train the critic model during IL phase to alleviate the performance drop issue when transitioning from IL to RL. Our system can be easily generalized to other agile mobile manipulation tasks such as agile catching and table tennis. Badminton is a competitive sport that requires high-speed reactions.

I've searched high and low for an enterprise-grade laptop that's portable, has the tools I need to keep my work secure, but that's also powerful enough to run graphically demanding 3D models, design software, and games. But up until now the closest thing I've come across is a creator laptop that can't compete in the portability stakes. Plus it looks too industrial for the office -- yuk! In fact, it destroys it completely, combining an extremely lightweight, thin, and attractive chassis, a powerful AI-capable chipset, and most importantly – the one thing that has me so in awe – graphics power that can rival a gaming laptop. How much graphics power am I talking about?

-- When pushing the speed limit for aggressive off-road navigation on uneven terrain, it is inevitable that vehicles may become airborne from time to time. During time-sensitive tasks, being able to fly over challenging terrain can also save time, instead of cautiously circumventing or slowly negotiating through. However, most off-road autonomy systems operate under the assumption that the vehicles are always on the ground and therefore limit operational speed. In this paper, we present a novel approach for in-air vehicle maneuver during high-speed off-road navigation. Based on a hybrid forward kinodynamic model using both physics principles and machine learning, our fixed-horizon, sampling-based motion planner ensures accurate vehicle landing poses and their derivatives within a short airborne time window using vehicle throttle and steering commands. We test our approach in extensive in-air experiments both indoors and outdoors, compare it against an error-driven control method, and demonstrate that precise and timely in-air vehicle maneuver is possible through existing ground vehicle controls. Off-road navigation presents various challenges that sharply contrast those encountered in on-road or indoor scenarios. In unstructured off-road environments, robots must detect and avoid obstacles, evaluate the traversability of varied terrain, and continuously adapt to complex vehicle-terrain interactions. Tackling all these challenges is essential to prevent terminal states that can jeopardize the mission and damage the robot, such as vehicle rollover and getting stuck.

Last year Samsung embraced artificial intelligence with its suite of Galaxy AI features. And while the new S25 Ultra is a bit faster, a little curvier and has a slightly bigger display than before, it's clear the company's primary focus was upgrading the software and machine learning capabilities of its top-spec flagship phone. Similar to its predecessor, the Galaxy S25 Ultra features a titanium frame. However, for 2025, Samsung wanted to buck tradition by making the phone a bit less boxy. So instead of a totally angular chassis, Samsung rounded off its corners. This gives the Ultra a bit more of a familial resemblance to its less expensive siblings (both the S25 and S25).

According to legend, Singer Vehicle Design founder and executive chairman Rob Dickinson was a young boy the first time his dad pointed out a Porsche 911. Dickinson turned that passion into a multi-million dollar business, reimagining classic Porsche models with his own twist. To be perfectly clear, Singer is not sponsored, approved, endorsed by, or in any way associated or affiliated with Porsche. Customers bring their own 911 to the Singer shop--not just any old 911, but an air-cooled 964 version model from 1989-1994--for a complete makeover. The cars are completely disassembled and modified around the original chassis with a process driven by Singer's obsessive attention to detail.

Methods for generative design of robot physical configurations can automatically find optimal and innovative solutions for challenging tasks in complex environments. The vast search-space includes the physical design-space and the controller parameter-space, making it a challenging problem in machine learning and optimisation in general. Evolutionary algorithms (EAs) have shown promising results in generating robot designs via gradient-free optimisation. Morpho-evolution with learning (MEL) uses EAs to concurrently generate robot designs and learn the optimal parameters of the controllers. Two main issues prevent MEL from scaling to higher complexity tasks: computational cost and premature convergence to sub-optimal designs. To address these issues, we propose combining morpho-evolution with intrinsic motivations. Intrinsically motivated behaviour arises from embodiment and simple learning rules without external guidance. We use a homeokinetic controller that generates exploratory behaviour in a few seconds with reduced knowledge of the robot's design. Homeokinesis replaces costly learning phases, reducing computational time and favouring diversity, preventing premature convergence. We compare our approach with current MEL methods in several downstream tasks. The generated designs score higher in all the tasks, are more diverse, and are quickly generated compared to morpho-evolution with static parameters.