Uniform and excessive herbicide application in modern agriculture contributes to increased input costs, environmental pollution, and the emergence of herbicide resistant weeds. To address these challenges, we developed a vision guided, AI-driven variable rate sprayer system capable of detecting weed presence, estimating canopy size, and dynamically adjusting nozzle activation in real time. The system integrates lightweight YOLO11n and YOLO11n-seg deep learning models, deployed on an NVIDIA Jetson Orin Nano for onboard inference, and uses an Arduino Uno-based relay interface to control solenoid actuated nozzles based on canopy segmentation results. Indoor trials were conducted using 15 potted Hibiscus rosa sinensis plants of varying canopy sizes to simulate a range of weed patch scenarios. The YOLO11n model achieved a mean average precision (mAP@50) of 0.98, with a precision of 0.99 and a recall close to 1.0. The YOLO11n-seg segmentation model achieved a mAP@50 of 0.48, precision of 0.55, and recall of 0.52. System performance was validated using water sensitive paper, which showed an average spray coverage of 24.22% in zones where canopy was present. An upward trend in mean spray coverage from 16.22% for small canopies to 21.46% and 21.65% for medium and large canopies, respectively, demonstrated the system's capability to adjust spray output based on canopy size in real time. These results highlight the potential of combining real time deep learning with low-cost embedded hardware for selective herbicide application. Future work will focus on expanding the detection capabilities to include three common weed species in South Dakota: water hemp (Amaranthus tuberculatus), kochia (Bassia scoparia), and foxtail (Setaria spp.), followed by further validation in both indoor and field trials within soybean and corn production systems.

Clothing for robots can help expand a robot's functionality and also clarify the robot's purpose to bystanders. In studying how to design clothing for robots, we can shed light on the functional role of aesthetics in interactive system design. We present a case study of designing a utility belt for an agricultural robot. We use reflection-in-action to consider the ways that observation, in situ making, and documentation serve to illuminate how pragmatic, aesthetic, and intellectual inquiry are layered in this applied design research project. Themes explored in this pictorial include 1) contextual discovery of materials, tools, and practices, 2) design space exploration of materials in context, 3) improvising spaces for making, and 4) social processes in design. These themes emerged from the qualitative coding of 25 reflection-in-action videos from the researcher. We conclude with feedback on the utility belt prototypes for an agriculture robot and our learnings about context, materials, and people needed to design successful novel clothing forms for robots.

In the context of a voice assistant system, steering refers to the phenomenon in which a user issues a follow-up command attempting to direct or clarify a previous turn. We propose STEER, a steering detection model that predicts whether a follow-up turn is a user's attempt to steer the previous command. Constructing a training dataset for steering use cases poses challenges due to the cold-start problem. To overcome this, we developed heuristic rules to sample opt-in usage data, approximating positive and negative samples without any annotation. Our experimental results show promising performance in identifying steering intent, with over 95% accuracy on our sampled data. Moreover, STEER, in conjunction with our sampling strategy, aligns effectively with real-world steering scenarios, as evidenced by its strong zero-shot performance on a human-graded evaluation set. In addition to relying solely on user transcripts as input, we introduce STEER+, an enhanced version of the model. STEER+ utilizes a semantic parse tree to provide more context on out-of-vocabulary words, such as named entities that often occur at the sentence boundary. This further improves model performance, reducing error rate in domains where entities frequently appear, such as messaging. Lastly, we present a data analysis that highlights the improvement in user experience when voice assistants support steering use cases.

Torrey Smith, Co-Founder of Endiatx, is changing the reputation endoscopies have for being uncomfortable. At Endiatx, they are developing a pill-sized robot that you swallow, which will then livestream your digestive system for a doctor to view. Our interviewer Abate dives in. Torrey Smith Torrey Smith is the Co-Founder & CEO of Endiatx, a medical robotics company that manufactures tiny robotic pills capable of active movement inside the human stomach with control over internet protocol. Prior to launching Endiatx, he developed medical devices in the areas of endometrial ablation, atherectomy, therapeutic hypothermia, sleep apnea, and vascular closure. An aerospace engineer by training, he takes a keen interest in the deep tech sector and is a proud mentor of up-and-coming founders at the Founder Institute. He is also the principal founder of the international arts collective known as Sextant, and he has had his art featured in the Smithsonian. Abate De Mey: Welcome to the robo hub podcast. Super excited to have you on here. So Torrey, could you introduce yourself a little bit? Well, you know, I originally originally studied aerospace engineering because my goal was to build the future of science fiction that I had read about as a kid. I had some relatives you know, come down with some gnarly health conditions. I lost an aunt to a brain cancer. I became very passionate about the world of medical devices and maybe more importantly, just health and technology and how we can merge those. Because I, I think if you asked a 14 year old kid who reads science fiction, what they think the future of healthcare looks like, they would probably say, oh, it's going to be like nano robots. That would go in like an army of tiny machines and kill any tumor. And then if you ask a doctor, Hey, I've got a glioblastoma. The doctor's going to say, well, we're going to cut an incision over here. I'm going to cut out a piece of your skull and put it in a steel dish. Then I'm going to go in and do my best to remove some of this brain tumor. And we're going to put you back together. We're going to put you on drugs. You know, we'll put you on chemo and you know, in six to nine months, you're going to be dead. And my question is simple.

Driscoll's is so secretive about its robotic strawberry picker it won't let photographers within telephoto range of it. But if you do get a peek, you won't see anything humanoid or space-aged. AgroBot is still more John Deere than C-3PO -- a boxy contraption moving in fits and starts, with its computer-driven sensors, graspers and cutters missing 1 in 3 berries. Such has been the progress of ag-tech in California, where despite the adoption of drones, iPhone apps and satellite-driven sensors, the hand and knife still harvest the bulk of more than 200 crops. Now, the $47-billion agriculture industry is trying to bring technological innovation up to warp speed before it runs out of low-wage immigrant workers.

Driscoll's is so secretive about its robotic strawberry picker it won't let photographers within telephoto range of it. But if you do get a peek, you won't see anything humanoid or space-aged. AgroBot is still more John Deere than C-3PO -- a boxy contraption moving in fits and starts, with its computer-driven sensors, graspers and cutters missing 1 in 3 berries. Such has been the progress of ag-tech in California, where despite the adoption of drones, iPhone apps and satellite-driven sensors, the hand and knife still harvest the bulk of more than 200 crops. Now, the $47-billion agriculture industry is trying to bring technological innovation up to warp speed before it runs out of low-wage immigrant workers.

A worker picks substrate-grown strawberries at the Driscoll's Inc. facility on the McGrath Ranch in Watsonville, Calif., on Sept. 19, 2016. Buoyed by an inexpensive migrant workforce, California has been the United States' agricultural mainstay for nearly a century, currently producing about 60 percent of the nation's fresh produce. But as the state's minimum wage approaches $15 an hour and competition from a growing Mexican economy mounts, producers face unprecedented operating costs and a workforce that has dropped by 60 percent since the 1990s. Add to this President Trump's moves to restrict immigration, which threatens to significantly curtail the sector's already depressed labor supply. Leading California-based growers like Driscoll's Berries and Taylor Farms are feeling the immediacy of Trump's executive orders, as millions of dollars of specialty crops are growing right now that will require a workforce to pick them at the end of the season. Together they spend over a billion dollars on labor each year.

A robotic strawberry picker built by AgroBot, a Spanish company. It's being tested in California as hiring laborers becomes increasingly difficult. But for one small corner, agricultural technology, it represents an opportunity. Farmers have been facing an increasingly tight labor market for years. The immigrant workforce that has long picked and packed the nation's fruits and vegetables move to better jobs as soon as they can, replaced by new immigrants.

On the first day of "camp," two dozen rising high school sophomores arrive at the Stanford Artificial Intelligence Laboratory's Outreach Summer program (SAILORS) giddy and ready to get started. The rigorous, two-week program is designed to encourage young women from underrepresented populations to get more involved in the field of science. High school students Ishla Zareef-Mustafa and Genaro Pamatz participate in an anatomy lab as part of the Stanford Medical Youth Science Program. On the last day of the summer residential Stanford Medical Youth Science Program (SMYSP), 24 high school students, surrounded by family members, friends and mentors, present the research they have been working on during the five-week summer program.These programs, which fall under the umbrella of Stanford Pre-Collegiate Studies, are designed to provide teenagers from underrepresented populations with an opportunity to explore careers in science, but also to build new relationships, while taking what they've learned back to their home communities. The SAILORS curriculum includes lectures, hands-on research projects and mentoring activities that are intended to educate and excite young women about artificial intelligence.