A walking robotic hand, from Fingers as Legs: Learning Self-Supported Locomotion and Manipulation with an Anthropomorphic Hand, Amirhossein Kazemipour, Hehui Zheng, Robert Katzschmann.
By Emmet Cole
In our monthly series, we showcase a selection of robotics stories from society, business, research and culture, as we track the field’s ongoing journey from specialized industrial machines to an increasingly visible social phenomenon.
More than five million industrial robots are now operating in factories worldwide, according to the International Federation of Robotics’ newly released World Robotics 2026 report.
Factories installed more than 600,000 industrial robots during 2025, an increase of 11% on the previous year. The global operational stock grew by 9%.
China continued to pull away from the rest of the world. Its factories installed 354,000 robots, accounting for 59% of the global total. Chinese manufacturers supplied 55% of their domestic market.
The United States overtook Japan to become the world’s second-largest market, with almost 38,500 installations. India installed nearly 10,500 robots, an increase of 15%. Europe moved in the opposite direction: installations fell by 8% in Germany, 11% in Italy, 8% in France and 15% in Spain.
IFR forecasts that worldwide installations will reach 655,000 units in 2026 and 806,000 in 2029.
Separately, Toyota told investors that modernizing factories operated by the company, its group businesses and major suppliers could require around 400,000 robots. The estimate includes replacement machines, new industrial robots, automated logistics systems and possible future humanoid deployments. Toyota said the programme could cost ¥1 trillion, or approximately $6.4 billion, annually from 2028, although it has not made a firm spending commitment.
Elsewhere, Universal Robots used the IMTS trade show in Chicago to launch Gen 7, a redesigned cobot platform built around greater computing power, tool-flange connectivity, sensor integration and support for AI-assisted applications.
In Rajasthan, India, women are being paid to record household activities from cameras worn on their heads, according to an investigation by Decode. Their footage captures everyday tasks such as cooking, cleaning and folding clothes from the worker’s point of view.
The recordings can be divided into individual actions and used to teach robots how people manipulate objects in real homes. This kind of data is especially valuable because household environments contain clutter, variation and improvised behaviour that are difficult to reproduce in a robotics laboratory.
Decode estimates that between 20,000 and 30,000 people may be doing this work in India, although that figure is an extrapolation rather than an official count. The article also stops short of establishing which robot companies ultimately receive the featured workers’ recordings.
International efforts to regulate autonomous weapons reached a significant point in September. Three years of discussions by the United Nations Group of Governmental Experts ended with growing support for formal negotiations on new international rules.
Human Rights Watch says 76 states now support negotiations on legally binding measures governing autonomous weapons systems. However, the group’s final report was weakened during negotiations. References to ethical concerns and to the design and development of the weapons were among the passages removed or reduced following objections from states including Russia and the United States.
The issue will now move to the Convention on Certain Conventional Weapons Review Conference in November, where states may decide whether to establish a negotiating mandate.
The Stop Killer Robots campaign argues that further discussion is no longer enough and that states should begin negotiating prohibitions and regulations.
Computer scientist Stuart Russell also entered the debate in a short UC Berkeley video. Russell called for a ban on small autonomous anti-personnel weapons, warning that machines capable of selecting and attacking people could become cheap, scalable weapons of mass destruction.
His intervention is more specific than a call to prohibit every military application of AI. It focuses on systems that can independently identify human targets and apply lethal force at scale.
A study published in the International Journal of Human–Computer Interaction examined whether perceptions of fairness can reduce conflict between people and security robots.
Xin Ye and Lionel Robert considered two forms of fairness. Distributive fairness concerns whether the robot’s decision or its consequences appear fair. Informational fairness concerns whether the robot provides an adequate explanation for that decision.
The researchers found that both can help reduce conflict and improve acceptance. The broader implication is that security robots may need to justify their actions as well as perform their assigned tasks effectively.
The results come from controlled human–robot interaction research, so they should not be treated as evidence from robots exercising authority in operational policing environments. Nevertheless, the study raises a question that will become more pressing as autonomous systems move into security roles: what does it mean for a machine’s exercise of authority to be regarded as legitimate?
Finally, researchers at ETH Zurich have taught an anthropomorphic robotic hand to walk on its fingertips.
The self-contained hand uses reinforcement-learning policies to crawl, steer and recover after falling. It can also support its own weight while pressing successive keyboard commands, and it can push an object towards targets using overhead visual feedback.
The work is currently an eight-page arXiv preprint, and separate policies are used for different tasks. Even so, it offers an inventive demonstration of a robot repurposing the same hardware for movement, balance and manipulation.
Introduced only a few weeks ago, Muse – an AI agent that can perform multiple tasks on a project without human supervision – has already been downloaded 10 million times.
Observes writer Asa Fitch: “Meta is already monetizing Muse’s growth, with subscription tiers costing $20 or $100 a month for heavier users.”
In other news and analysis on AI writing:
*ChatGPT to Challenge Meta Muse with AI Agent ‘Dots:’ Smarting from the meteoric rise in popularity of Meta’s new AI agent Muse, ChatGPT is rolling out a new AI agent to compete with Muse – ChatGPT Dots.
Like Muse, Dots is designed to work independently on a multi-step project – such as researching an article by visiting multiple Web sites, analyzing the results, creating an article outline and then writing the article.
The downside: So far, Dots will only be available on ChatGPT’s Pro Plan, which starts at $100/month.
*Anthropic Readies Limited Release of Its Competition to Muse: ‘Claude Duet:‘ Anthropic’s answer to the new AI agents Muse and Dots – Claude Duet – is being released as a ‘research preview’ for Claude subscribers on the Pro or Max plans.
Observes writer Megan Cerullo: “Each morning, Claude Duet sends its user a daily brief outlining the day’s most important items. For the agent to have context, it must be connected to a person’s apps, like their email accounts and calendars.”
You can sign up for a wait list to be among the first to use Claude here.
*400+ Publications Now Feeding Google’s AI Generated News: Add CNN and the Financial Times to the growing list of publications allowing Google to rewrite their news for use in Google news features, summaries and audio podcasts.
One of the benefits of the deal for the publishers: The original news stories may show up higher in Google search engine returns such as AI Overviews and AI Mode.
*DeepSeek Backlash: Banned for Use by Civilian Federal Agencies: Fearing that U.S. data could be forwarded along to the Chinese government, the U.S. has banned use of Chinese phenom AI model DeepSeek by civilian federal agencies.
The ban also covers use of third-party tools that have been built on DeepSeek models.
DeepSeek has been a major disrupter in the AI space, often offering pricing much less expensive than AI pricing offered by U.S. titans like OpenAI, Anthropic and Google.
*China Maintains Ban on Major AI Models from Google, Anthropic and OpenAI: While China has been angered by the U.S.’ decision to expand a ban on the use of DeepSeek, the fact is that the Chinese government has banned major AI from the U.S. for years.
Observes writer Maximilian Schreiner: “ChatGPT, Claude, and Gemini aren’t approved there.”
Plus, that ban applies to the entire Chinese market – not just Chinese government users.
*Digital Design Platform Canva Releases Its Own AI Image-Maker: Writers who regularly use Canva for article layouts will want to check-out the platform’s new AI image-maker, Canva Craft.
Observes writer Ivan Mehta: “Canva has used a bunch of image models for generation in its platform, so it’s likely that the new models will help save API costs for the company. It said that besides costs, the new model also allows the platform to have better quality and speed control.”
Currently, approximately 265 million digital creators use Canva.
*Amazon Launches $75 AI Note-Taker: Add Amazon to the growing list of manufacturers offering wearable tech that records, analyzes and summarizes conversations.
Dubbed “Bee Frontier,” the tech can be worn on the wrist or clothing and offers an optional, $11.99 monthly subscription that comes with longer-term memory, app integrations and more.
Other companies competing in the same space include Plaud, SwitchBot, Omi and Vocci.
*New AI Plugins Customize AI for 501 Occupations: Complete AI Training is out with a new plugin that customizes popular AI for hundreds of highly specific occupations.
Dubbed “The AI Coach,” the plugin offers news, suggestions and short AI lessons that target the user’s job description.
So far, AI Coach is designed to work with ChatGPT, Claude, Microsoft 365 Copilot, Codex, Cursor and Grok.
*AI BIG PICTURE: Microsoft’s Bill Gates on AI: “This Is by Far the Most Dangerous Technology Ever Invented:” Microsoft co-founder Bill Gates has joined the ranks of Geoffrey Hinton – the godfather of AI – who believe society needs to get a handle on the development of AI right now.
Essentially, Gates – in this extremely in-depth interview with The Verge – warns that things could easily spin-out-of-control if we allow AI leaders to simply develop AI at breakneck speed.
Observes Gates: “The people close to this (AI researchers) are very concerned about misuse, job impacts, social relationship impacts — and I’d say it’s impressive to have an industry that’s speaking out as vividly as they are. Some outsiders say, ‘get better PR companies.’ But that ignores the fact that these warnings are the truth.”
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.
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Claire chatted to Jan Petrš from École Polytechnique Fédérale de Lausanne (EPFL) and Déploya Robotics about novel robot designs for construction and architecture.
Jan Petrš is a roboticist, architect, and entrepreneur working at the intersection of robotics and construction. He is currently the co-founder of Déploya Robotics, an EPFL spin-off developing deployable robotic systems for automated façade installation. Previously, he was a Postdoctoral Researcher at EPFL’s Laboratory of Intelligent Systems, where he focused on tensegrity robotics. His PhD research at the Czech Technical University focused on developing a robotic system combining reconfigurable and soft robotics for architectural and construction applications.
Our position on long-running, autonomous agents, and why most business processes need a specialist instead.
Picture the person who has to sign off on your first autonomous agent. It might be your head of audit, your CISO, or the risk lead for the business unit that wants it. They ask one question: when this agent does something, who authorized it? If nobody can answer that cleanly, the agent stays in pilot.
That chair is where a lot of agentic AI gets stuck. In DataRobot’s Unmet AI Needs Survey 2026 of 413 agentic AI stakeholders, the question of whether existing access controls and catalogs are adequate to govern agents came out a coin flip: 49% said yes, 51% said no. Governance also topped the list of challenges companies expect when scaling from one agent to 100 or more, cited by 61%. And launch isn’t the finish line, since 94% of organizations hit Day 2 operational issues after deploying agentic AI. The workaround most teams reach for is a human clicking approve on every meaningful action. That doesn’t automate anything. It adds a new interface to the same bottleneck.
And the problem just got bigger, because a new class of agent has gone mainstream. These agents don’t wait to be invoked. They keep memory across restarts, run a loop between your messages, and act on their own initiative. Open-source projects like Hermes Agent popularized the pattern, and the big labs have followed: xAI shipped Grok Bot in August, Meta shipped Muse in September, and OpenAI launched Dots on September 29, and Anthropic launched Claude Duet on October 21.
That’s useful, and it’s a very different risk surface from a bounded pipeline that runs, answers, and exits. Our view is that the right level of autonomy depends on the problem, and you shouldn’t need a different platform for each answer. Here’s how we think about it.
People tend to lump them together, but they’re worth separating. A long-running agent never turns off and keeps state across sessions, so it gets better at your environment over time instead of starting cold on every run. An autonomous agent can decide to act without a human approving that specific action: pick up a task, call a tool, spend tokens, write to a system of record, or message someone.
A dedicated agent flow, like a RAG pipeline or a ticket classifier, is usually neither. It’s invoked, runs a mostly fixed set of steps, returns an answer, and remembers only what was in the prompt. Hermes-style agents are both.
You’d want both when the problem is a standing responsibility instead of a single request. Take month-end close: pull the reconciliations, flag exceptions, chase owners, and escalate only what needs a person. You could stitch that together with scheduled jobs calling narrow pipelines, but you’d be hand-building the scheduling, the state, and the judgment about what’s worth escalating. A long-running agent handles that natively, and its context compounds as it learns your systems and conventions.
The trade is explicit. You give up a small, auditable surface area for a bigger one that can do more without you. That’s worth it for judgment-heavy, long-horizon work. It usually isn’t worth it for a well-defined, high-volume, cost-sensitive task that a guarded flow already handles at a fraction of the token spend.
Grok Bot, Muse, and Dots make the same bet from three different labs: give every user an agent that keeps working on their behalf, with broad access and a standing goal. Grok Bot and Muse each give the agent its own cloud machine, and Dots connects to more than 4,000 apps and is pitched as able to do nearly anything. That breadth is the point. These are generalists, the office administrators of the agent world, and they’re impressive precisely because they aren’t specialized for anything.
That’s a fine trade for a person delegating their own errands. It’s a shakier one for a business process. A generalist reaches for a frontier model because it can’t know in advance which step is hard, so work a small model could do reliably runs on the most expensive one, on a loop. Breadth of access is also breadth of blast radius, and the vendors say so themselves: Grok Bot itself calls its shared machine, logins, and files “a real blast radius.” Consumer-first products also come with consumer-first defaults on data use and regional availability, which is a different bar from what your regulator expects. And their guardrails lean heavily on per-user approvals, while an enterprise needs policy that applies to every agent and every action whether or not someone remembered to click deny.
The alternative is a specialist. Compare a general-purpose coffee machine with a barista trained for one job: the same drink, made the same way, quickly, at a known cost, every time. A specialist agent knows its process, uses only the tools that process needs, and runs each step on the model that step actually needs.
An agent that acts on its own, remembers things, and keeps running between check-ins needs controls a stateless pipeline never did. It holds long-lived credentials because it has to act later, unattended, which makes those credentials a permanent target. Prompt injection turns into privilege escalation: a pipeline that reads a poisoned document produces a bad answer, but a long-running agent that reads one can open a PR, leak a secret, or message a customer. Memory becomes a durable attack surface, since one false fact written into it shapes every future session. And because behavior drifts as memory and skills evolve, a one-time code review isn’t enough; tracing has to be always on.
Then there’s identity, which nobody has solved cleanly yet. A human teammate logs in as themselves, and their access leaves when they do. A team agent is provisioned once and used by many, so when something goes wrong, “who authorized this?” has no clean answer. Its permissions tend to ratchet up and never come back down. It can spawn sub-agents or act on automated triggers, producing actions with no human originator at all. And it often ends up on one shared service identity, which erases per-user attribution right when an incident review needs it. The right posture looks like how you’d treat a highly privileged contractor: a named identity where possible, scoped and reviewed grants, short-lived tokens, and an audit trail that separates “the agent did this” from “the agent did this because a specific person asked.”
Autonomy is worth having, and governance is what makes it safe to adopt. That’s why it has to come first.
We see two ends to this, and we’re building for both.
For the problems that are truly open-ended, where a self-learning agent with long-running memory earns its keep, run that agent inside your own governed perimeter: your data, your infrastructure, tracing from day one. Our Labs team is already showing how that works (more on that below). Save it for work where true autonomy is worth the spend.
For everything else, and that’s most critical business problems, you don’t need a generalist or open-ended autonomy. You need a specialist that executes the process, not one that drafts suggestions about it, with governance and approval gates built in. That’s the default, and it’s where we lead.
Decision Agents are DataRobot’s specialists. Each one is built to make and carry out the decisions behind a specific business process, the ones that move a number the business already reports, across the systems, teams, and partners that process touches. Where a generalist is designed to handle anything, a Decision Agent is designed to handle one process correctly, every time, at a predictable cost.
Governance comes from the Agent Workforce Platform they run on, so it’s there from the first run instead of added after. Through the DataRobot LLM gateway, you choose the model for each task, which means routine steps don’t have to run on the most expensive frontier model. Guard models check the steps that matter, and human approval gates sit wherever you decide a person needs to sign off. Each agent gets its own identity and a named owner, and its secrets live in the DataRobot credentials store instead of in code. OpenTelemetry traces of model and tool calls give your audit team a record of what the agent did and why. And Decision Agents run where you need them: in the cloud, on-prem, or air-gapped, on your own infrastructure.
You get outcomes you can predict and spend you can plan for, without the standing risk surface of a long-running generalist. To see what that looks like, watch a scenario that takes a $2.5B supply chain crisis to competitive advantage in 72 hours.
For the open-ended end of the spectrum, DataRobot Labs has published two experimental open-source projects that show what a governed perimeter looks like in practice.
Hermes Agent on DataRobot runs the open-source Hermes agent as a DataRobot workload. It’s experimental and community-maintained, not an officially supported DataRobot product, but it shows the full pattern end to end. It deploys from git with the DataRobot CLI, and you can turn on automatic redeploys on every push. The web chat sits behind DataRobot login and sharing, and the optional Slack bot only answers an allow-list of users. Model calls go through the DataRobot LLM gateway, so the agent holds no provider API keys and you can pick the model per job. Secrets live in the DataRobot credentials store, and on GitHub the agent acts as a GitHub App with short-lived installation tokens. Model calls and tool calls are traced with OpenTelemetry.
datarobot-sync is the companion tool, also an experimental Labs project. It copies only what changed between a local directory and DataRobot Files, so an always-on agent keeps its skills, memory, and sessions across restarts.
Long-running, autonomous agents have a real future for the problems that are truly open-ended and worth the spend. They need governance, identity, and controls in place from day one, before the first incident rather than after it. For everything else, a specialist running a guarded, reviewable flow is the better engineering and business decision. Autonomy where it pays off. Control where it counts.
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