Developed by Texas A&M University engineering students, this AI-powered robotic dog doesn't just follow commands. Designed to navigate chaos with precision, the robot could help revolutionize search-and-rescue missions, disaster response and many other emergency operations. Sandun Vitharana, an engineering technology master's student, and Sanjaya Mallikarachchi, an interdisciplinary engineering doctoral student, spearheaded the invention of the robotic dog. It can process voice commands and uses AI and camera input to perform path planning and identify objects. A roboticist would describe it as a terrestrial robot that uses a memory-driven navigation system powered by a multimodal large language model (MLLM).

When you purchase through links in our articles, we may earn a small commission. These appliances don't depend on smart speakers for voice control Emerson Smart's new appliances respond to voice commands, but they don't need a smart speaker--or even a broadband connection--to pull off the trick. Smart appliances that can be controlled with voice commands are nothing new, but IAI Smart is showing a new line of Emerson Smart appliances at CES that respond to voice commands. They don't need a smart speaker in the middle, and they don't rely on a broadband connection, an app, or anything other infrastructure--everything is processed locally. If you're leery of the privacy and security vulnerabilities of IoT devices, this could be the answer.

Here's how I cut down my family's reliance on Alexa. Until recently, my smart home setup was in chaos. After years of testing, buying, and upgrading to the latest smart home gadgets in an attempt to make my life easier, it became a bloated mess that was actually making it more complicated. My Alexa, Google Home, and Apple Home apps were awash with dead devices, duplicates, and automations that simply didn't work. My Hue Bridge, trying desperately to tie it all together, was creaking at the seams.

-- Air traffic controllers (ATCOs) issue high - intensity voice commands in dense airspace, where accurate workload modeling is critical for safety and efficiency. This paper proposes a multimodal deep learning framework that integrates structured data, trajectory sequences, and image features to estimate two key parameters in the ATCO command lifecycle: the time offset between a command and the resulting aircraft maneuver, and the command duration. A hi gh - quality dataset was constructed, with maneuver points detected using sliding window and histogram - based methods. A CNN - Transformer ensemble model was developed for accurate, generalizable, and interpretable predictions. By linking trajectories to voice commands, this work offers the first model of its kind to support intelligent command generation and provides practical value for workload assessment, staffing, and scheduling. A. Background As global air traffic demand increases, airspace operations have become more complex and congested, presenting major challenges for air traffic control (ATC) systems. Although surveillance and communication technologies have improved, ATC performance still largely depends on human operators, particularly air traffic controllers (ATCOs), who monitor flights, assess conditions, and issue maneuver instructions to ensure safe and efficient operations. This human bottleneck has become a key constraint on ATC efficiency and safety, emphasizing the importance of quantifying task intensity and evaluating workload to support fatigue management, staff scheduling, and the development of in telligent ATC solutions . Early studies on ATCO workload modeling primarily focused on statistical methods and subjective assessments such as NASA Task Load Index (NASA - TLX) [1] .

Smart lights that react to voice commands spoken to smart speakers are old hat, but a smart light with a built-in AI microphone? Showing off its wares at the IFA trade show in Berlin this week, Germany-based smart device manufacturer Lepro is teeing up a quartet of "AI Lighting Pro" lights that can set the mood based on your natural-language prompts--anything from "Give me an Iron Man vibe" to "Set a cyberpunk city theme." Each of the lights features a built-in microphone that captures your commands (you must say the "Hey Lepro" wake phrase first) and processes them using Lepro's new LightGPM AI engine, a large language model that's trained on "color psychology and lighting design," Lepro says. The AI then delivers an "ideal" multi-color lighting scene based on your voice prompt. We've seen plenty of smart lights with AI-powered light scene bots before; Philips Hue is integrating one into the Hue app, and Govee and Nanoleaf have their own versions.

Early detection of cognitive decline is crucial for enabling interventions that can slow neurodegenerative disease progression. Traditional diagnostic approaches rely on labor-intensive clinical assessments, which are impractical for frequent monitoring. Our pilot study investigates voice assistant systems (VAS) as non-invasive tools for detecting cognitive decline through longitudinal analysis of speech patterns in voice commands. Over an 18-month period, we collected voice commands from 35 older adults, with 15 participants providing daily at-home VAS interactions. To address the challenges of analyzing these short, unstructured and noisy commands, we propose Cog-TiPRO, a framework that combines (1) LLM-driven iterative prompt refinement for linguistic feature extraction, (2) HuBERT-based acoustic feature extraction, and (3) transformer-based temporal modeling. Using iTransformer, our approach achieves 73.80% accuracy and 72.67% F1-score in detecting MCI, outperforming its baseline by 27.13%. Through our LLM approach, we identify linguistic features that uniquely characterize everyday command usage patterns in individuals experiencing cognitive decline.

Breakthroughs, discoveries, and DIY tips sent every weekday. Our smartphones have transformed the way we take photos and videos and our relationship to these digital memories. Most of us will snap at least some pictures and clips every day with the gadget that's always close at hand. If you want to get more creative with photos on your phone, you can. Sometimes you're going to want to take a picture remotely, without your phone in your hand and your finger over the shutter button--maybe you're taking a wide shot of a large group, or you want to capture a lot of your surroundings.

The integration of Large Language Models (LLMs), such as GPT, in industrial robotics enhances operational efficiency and human-robot collaboration. However, the computational complexity and size of these models often provide latency problems in request and response times. This study explores the integration of the ChatGPT natural language model with the Robot Operating System 2 (ROS 2) to mitigate interaction latency and improve robotic system control within a simulated Gazebo environment. We present an architecture that integrates these technologies without requiring a middleware transport platform, detailing how a simulated mobile robot responds to text and voice commands. Experimental results demonstrate that this integration improves execution speed, usability, and accessibility of the human-robot interaction by decreasing the communication latency by 7.01\% on average. Such improvements facilitate smoother, real-time robot operations, which are crucial for industrial automation and precision tasks.

Recent progress in mixed reality (MR) and robotics is enabling increasingly sophisticated forms of human-robot collaboration. Building on these developments, we introduce a novel MR framework that allows multiple quadruped robots to operate in semantically diverse environments via a MR interface. Our system supports collaborative tasks involving drawers, swing doors, and higher-level infrastructure such as light switches. A comprehensive user study verifies both the design and usability of our app, with participants giving a "good" or "very good" rating in almost all cases. Overall, our approach provides an effective and intuitive framework for MR-based multi-robot collaboration in complex, real-world scenarios.

Leviton, one of the biggest electrical component manufacturers in the world, makes high-quality products and offers a comprehensive collection of Z-Wave-compatible devices in addition to this Z-Wave 800 dimmer and switch. Smart lighting controls that operate over Wi-Fi are great, because they don't require a hub; they connect directly to your router. The downside is that they must compete with all the other clients on your home network: Your computers, gaming consoles, media streamers, smart speakers, home security cameras, smart plugs, and many, many more. I live in a very small home--less than 800 square feet--but there are still more than 80 devices connected to the Eero 6 router in my Ring Alarm Pro. Given that the Eero 6's practical limit is 128 clients, there just isn't a lot of room for light switches and dimmers.