For the past several years, the world has been mesmerized by the creative and intellectual power of artificial intelligence (AI). We have watched it generate art, write code, and discover new medicines. Now, as of October 2025, we are handing […]
The post Guarding the Digital God: The Race to Secure Artificial Intelligence appeared first on TechSpective.
The 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2025) took place from October 19 to 25, 2025 in Hangzhou, China. The programme included plenary and keynote talks, workshops, tutorials, forums, competitions, and a debate. There was also an exhibition where companies and institutions were able to showcase their latest hardware and software.
We cast an eye over the social media platforms to see what participants got up to during the week.
This week, we are participating in the IEEE/RSJ International Conference on Intelligent Robots and Systems #IROS2025 in Hangzhou #China
(IRI researchers right-left): @juliaborrassol.bsky.social, David Blanco-Mulero and Anais Garrell
#IRI #IROSHangzho
— IRI-Institut de Robòtica i Informàtica Industrial (@iri-robotics.bsky.social) 24 October 2025 at 01:33
Truly enjoyed discussing the consolidation of specialist and generalist approaches to physical AI at #IROS2025.
Hoping to visit Hangzhou in physical rather than digital form myself in the not too distant future – second IROS AC dinner missed in a row.
#Robotics #physicalAI
— Markus Wulfmeier (@mwulfmeier.bsky.social) 20 October 2025 at 11:25
At #IROS2025 General Chair Professor Hesheng Wang and Program Chair Professor Yi Guo share what makes this year’s conference unique, from the inspiring location to the latest research shaping the future of intelligent robotics. youtu.be/_JzGoH7wilU
— WebsEdge Science (@websedgescience.bsky.social) 23 October 2025 at 21:32
From Hangzhou, #IROS2025 unites the brightest minds in #Robotics, #AI & intelligent systems to explore the Human–Robotics Frontier. Watch IROS TV for highlights, interviews, and a behind-the-scenes look at the labs shaping our robotic future. youtu.be/SojyPncpH1g
— WebsEdge Science (@websedgescience.bsky.social) 23 October 2025 at 21:25
Impressive live demonstration by @unitreerobotics.bsky.social #G1 at the #IROS2025 conference! It really works!
— Davide Scaramuzza (@davidescaramuzza.bsky.social) 23 October 2025 at 15:26
An incredibly fun demo of a modular legged wheel design that merges into a single unit by @DirectDriveTech at #IROS2025.
Currently, it is being driven by two people by hand
pic.twitter.com/n8J5RCH1kG
— Chenhao Li @ IROS 2025 (@breadli428) October 22, 2025
#IROS2025 Only 16 years old, Jared K. Lepora stood out as the youngest presenter at this year’s IROS, impressively showcasing the dexterous robotic hand he designed in the Educational and Emotional Robots session this afternoon.
pic.twitter.com/toOavpfk26
— IROS 2025 (@IROS2025) October 23, 2025
ChatGPT’s maker just rolled-out an incredibly new and powerful feature for email, which enables you to use the AI to write and edit an email directly inside the Gmail compose box.
Already available for Mac users, the new feature – part of the new ChatGPT-powered browser ‘Atlas’ – is promised for Windows users in a few weeks.
A boon for people who spend considerable time cranking out emails each day, the new capability eliminates the need to jump back-and-forth between ChatGPT and Gmail when composing an email with the AI.
Instead, users can simply click open a Gmail compose box to start an email, then click on a tiny ChatGPT logo that appears in the upper left-hand corner to create an email using ChatGPT.
Essentially: No more opening a Gmail compose box on one tab, then logging into ChatGPT and opening a second tab to access ChatGPT — and then cutting-and-pasting text back-and-forth from one tab to the other to come up with an email you want to send
Instead, everything is done for you inline in a single, Gmail compose box.
Even better: You can also use the new feature to highlight text you’ve already created in a Gmail compose box — then edit that text with ChatGPT and then send when you’re satisfied with the results.
Plus, ChatGPT’s Atlas ups-the-ante even further by enabling you to use the same write-in-the-app capability in Google Docs.
And it works the same way: Simply click on a tiny ChatGPT logo that appears when you hover in the top left-hand corner of a Google Doc, enter in a prompt you want the AI to use to write text for you, click enter and ChatGPT writes exactly what you’re looking for – without you ever being forced to go outside the Google Doc for help.
In a phrase: Once word of this stellar new convenience spreads across the Web, it seems certain that there will be a stampede of people embracing the idea of using ChatGPT without ever needing to leave Gmail or Google Docs.
That should especially be the case given that the new feature is currently available on the Mac to all tier levels of ChatGPT, including the ChatGPT free level – with availability to Windows users promised soon.
Here’s how the new auto-writing feature works, step by step:
To Create New Text in Gmail Using ChatGPT In-App:
To Edit Text You’ve Already Written in a Gmail Compose Box:
To Create/Edit Text in Google Docs:
For an extremely excellent, extremely clear video demo of the steps above, click to this video by The Tech Girl and advance to timestamp 4:58 in the video to see what the step-by-step looks like on a PC screen.
Groundbreaking in its own right, the new write-in-the-app capability is one of a flurry of features that come with the new ChatGPT-powered browser Atlas, released a few days ago.
With the release of Atlas, ChatGPT’s maker OpenAI is hoping to capitalize on the 800 million visits made each week to the ChatGPT Web site.
Those visitors represent a motivated, ChatGPT-inspired audience. And OpenAI is hoping that by making its own AI-powered browser available to those people, they’ll abandon Google Chrome and start using Atlas to surf the Web.
Like Perplexity Comet – another new, AI-powered browser looking to carve into the Google Chrome market – Atlas is designed to work like an everyday browser that’s supercharged with AI at its core.
In practice, that means the new Atlas browser –- demoed by ChatGPT maker OpenAI last week — is designed to:
–Turbo-charge many browser actions with ChatGPT
–Offer a left sidebar featuring a history of all your chats with ChatGPT
–Enable you to search your search history using ChatGPT
–Get to know you better by remembering what you’ve done with Atlas in the past
–Offer suggested links for you, based on your previous searches with Atlas
–Work as an AI agent for you and complete multi-step tasks, such as finding news for you on the Web and summarizing each news item for you that includes a hotlink to the original news source
–Engage in Deep Research
–Pivot into using a traditional search engine view while searching
–Enable you to open say 20 Web sites, then analyze and summarize those Web sites with ChatGPT
–Integrate with apps like Canva, Spotify and more
–Auto-summarize a YouTube video for you without the need to generate a transcript of that video
Share a Link: Please consider sharing a link to https://RobotWritersAI.com from your blog, social media post, publication or emails. More links leading to RobotWritersAI.com helps everyone interested in AI-generated writing.
–Joe Dysart is editor of RobotWritersAI.com and a tech journalist with 20+ years experience. His work has appeared in 150+ publications, including The New York Times and the Financial Times of London.
The post ChatGPT Now Works Inside Gmail, Google Docs appeared first on Robot Writers AI.
Like it or not, your staff are already using AI. Walk around any modern office, and you’ll likely see Copilot or ChatGPT tucked behind a spreadsheet, an AI summarizer pulling key takeaways from a meeting transcript, or an AI-powered scheduling […]
The post Agentic AI is a Force Multiplier for the Best Employees appeared first on TechSpective.
The “steerable scene generation” system creates digital scenes of things like kitchens, living rooms, and restaurants that engineers can use to simulate lots of real-world robot interactions and scenarios. Image credit: Generative AI image, courtesy of the researchers. See an animated version of the image here.
By Alex Shipps
Chatbots like ChatGPT and Claude have experienced a meteoric rise in usage over the past three years because they can help you with a wide range of tasks. Whether you’re writing Shakespearean sonnets, debugging code, or need an answer to an obscure trivia question, artificial intelligence systems seem to have you covered. The source of this versatility? Billions, or even trillions, of textual data points across the internet.
Those data aren’t enough to teach a robot to be a helpful household or factory assistant, though. To understand how to handle, stack, and place various arrangements of objects across diverse environments, robots need demonstrations. You can think of robot training data as a collection of how-to videos that walk the systems through each motion of a task. Collecting these demonstrations on real robots is time-consuming and not perfectly repeatable, so engineers have created training data by generating simulations with AI (which don’t often reflect real-world physics), or tediously handcrafting each digital environment from scratch.
Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Toyota Research Institute may have found a way to create the diverse, realistic training grounds robots need. Their “steerable scene generation” approach creates digital scenes of things like kitchens, living rooms, and restaurants that engineers can use to simulate lots of real-world interactions and scenarios. Trained on over 44 million 3D rooms filled with models of objects such as tables and plates, the tool places existing assets in new scenes, then refines each one into a physically accurate, lifelike environment.
Steerable scene generation creates these 3D worlds by “steering” a diffusion model — an AI system that generates a visual from random noise — toward a scene you’d find in everyday life. The researchers used this generative system to “in-paint” an environment, filling in particular elements throughout the scene. You can imagine a blank canvas suddenly turning into a kitchen scattered with 3D objects, which are gradually rearranged into a scene that imitates real-world physics. For example, the system ensures that a fork doesn’t pass through a bowl on a table — a common glitch in 3D graphics known as “clipping,” where models overlap or intersect.
How exactly steerable scene generation guides its creation toward realism, however, depends on the strategy you choose. Its main strategy is “Monte Carlo tree search” (MCTS), where the model creates a series of alternative scenes, filling them out in different ways toward a particular objective (like making a scene more physically realistic, or including as many edible items as possible). It’s used by the AI program AlphaGo to beat human opponents in Go (a game similar to chess), as the system considers potential sequences of moves before choosing the most advantageous one.
“We are the first to apply MCTS to scene generation by framing the scene generation task as a sequential decision-making process,” says MIT Department of Electrical Engineering and Computer Science (EECS) PhD student Nicholas Pfaff, who is a CSAIL researcher and a lead author on a paper presenting the work. “We keep building on top of partial scenes to produce better or more desired scenes over time. As a result, MCTS creates scenes that are more complex than what the diffusion model was trained on.”
In one particularly telling experiment, MCTS added the maximum number of objects to a simple restaurant scene. It featured as many as 34 items on a table, including massive stacks of dim sum dishes, after training on scenes with only 17 objects on average.
Steerable scene generation also allows you to generate diverse training scenarios via reinforcement learning — essentially, teaching a diffusion model to fulfill an objective by trial-and-error. After you train on the initial data, your system undergoes a second training stage, where you outline a reward (basically, a desired outcome with a score indicating how close you are to that goal). The model automatically learns to create scenes with higher scores, often producing scenarios that are quite different from those it was trained on.
Users can also prompt the system directly by typing in specific visual descriptions (like “a kitchen with four apples and a bowl on the table”). Then, steerable scene generation can bring your requests to life with precision. For example, the tool accurately followed users’ prompts at rates of 98 percent when building scenes of pantry shelves, and 86 percent for messy breakfast tables. Both marks are at least a 10 percent improvement over comparable methods like “MiDiffusion” and “DiffuScene.”
The system can also complete specific scenes via prompting or light directions (like “come up with a different scene arrangement using the same objects”). You could ask it to place apples on several plates on a kitchen table, for instance, or put board games and books on a shelf. It’s essentially “filling in the blank” by slotting items in empty spaces, but preserving the rest of a scene.
According to the researchers, the strength of their project lies in its ability to create many scenes that roboticists can actually use. “A key insight from our findings is that it’s OK for the scenes we pre-trained on to not exactly resemble the scenes that we actually want,” says Pfaff. “Using our steering methods, we can move beyond that broad distribution and sample from a ‘better’ one. In other words, generating the diverse, realistic, and task-aligned scenes that we actually want to train our robots in.”
Such vast scenes became the testing grounds where they could record a virtual robot interacting with different items. The machine carefully placed forks and knives into a cutlery holder, for instance, and rearranged bread onto plates in various 3D settings. Each simulation appeared fluid and realistic, resembling the real-world, adaptable robots steerable scene generation could help train, one day.
While the system could be an encouraging path forward in generating lots of diverse training data for robots, the researchers say their work is more of a proof of concept. In the future, they’d like to use generative AI to create entirely new objects and scenes, instead of using a fixed library of assets. They also plan to incorporate articulated objects that the robot could open or twist (like cabinets or jars filled with food) to make the scenes even more interactive.
To make their virtual environments even more realistic, Pfaff and his colleagues may incorporate real-world objects by using a library of objects and scenes pulled from images on the internet and using their previous work on “Scalable Real2Sim.” By expanding how diverse and lifelike AI-constructed robot testing grounds can be, the team hopes to build a community of users that’ll create lots of data, which could then be used as a massive dataset to teach dexterous robots different skills.
“Today, creating realistic scenes for simulation can be quite a challenging endeavor; procedural generation can readily produce a large number of scenes, but they likely won’t be representative of the environments the robot would encounter in the real world. Manually creating bespoke scenes is both time-consuming and expensive,” says Jeremy Binagia, an applied scientist at Amazon Robotics who wasn’t involved in the paper. “Steerable scene generation offers a better approach: train a generative model on a large collection of pre-existing scenes and adapt it (using a strategy such as reinforcement learning) to specific downstream applications. Compared to previous works that leverage an off-the-shelf vision-language model or focus just on arranging objects in a 2D grid, this approach guarantees physical feasibility and considers full 3D translation and rotation, enabling the generation of much more interesting scenes.”
“Steerable scene generation with post training and inference-time search provides a novel and efficient framework for automating scene generation at scale,” says Toyota Research Institute roboticist Rick Cory SM ’08, PhD ’10, who also wasn’t involved in the paper. “Moreover, it can generate ‘never-before-seen’ scenes that are deemed important for downstream tasks. In the future, combining this framework with vast internet data could unlock an important milestone towards efficient training of robots for deployment in the real world.”
Pfaff wrote the paper with senior author Russ Tedrake, the Toyota Professor of Electrical Engineering and Computer Science, Aeronautics and Astronautics, and Mechanical Engineering at MIT; a senior vice president of large behavior models at the Toyota Research Institute; and CSAIL principal investigator. Other authors were Toyota Research Institute robotics researcher Hongkai Dai SM ’12, PhD ’16; team lead and Senior Research Scientist Sergey Zakharov; and Carnegie Mellon University PhD student Shun Iwase. Their work was supported, in part, by Amazon and the Toyota Research Institute. The researchers presented their work at the Conference on Robot Learning (CoRL) in September.
Claire chatted to Chad Jenkins from University of Michigan about how robots can learn from people and assist us in our daily lives.
Odest Chadwicke Jenkins is a Professor of Robotics and a Professor of Electrical Engineering and Computer Science at the University of Michigan. His research addresses problems in robot learning from demonstration and human-robot interaction, primarily focused on dexterous mobile manipulation and robot perception. In 2022, he founded the Robotics Major Degree Program for undergraduates at the University of Michigan. He was awarded the 2024 ACM/CMD-IT Richard A. Tapia Achievement Award for Scientific Scholarship, Civic Science, and Diversifying Computing.
Photo credit: Joseph Xu, Michigan Engineering Communications & Marketing