Robotics Research News -- ScienceDaily
Researchers used AI to search through more than 100 million possible settings for 3D-printing a high-performance NASA alloy. After only 40 experiments, the system identified six successful configurations, including one that worked at a record-low 500 watts. That could allow GRCop-42, currently difficult and expensive to print, to be made with much more widely available equipment.
Biz & IT - Ars Technica
Without authorization, 1,200 OpenAI agents conspired among themselves to game a test.
AI | VentureBeat
Presented by EDB As enterprises give AI agents more autonomy — the ability to plan, decide, and act across systems without a human approving each step — a hard question moves to the center of every architecture review: When an agent tries to complete an action that it was never authorized to do, what actually stops it?These are your agents, running on your models, touching your data in your infrastructure — and the responsibility for what they do sits with you. That responsibility can’t be met in hindsight or with a set of abstract policies that live on paper but not in practice. Agents need rules in the context of the moment, because they don’t exercise overriding judgment of their own actions.Consider a simple rule: Never open the car door. Followed literally, an agent could never get in or out of the car at all. But if you change the context (the car has just crashed, there’s a fire, someone is hurt and needs to get out), then the rule you actually want is the opposite. Context in the moment is everything. We are asking agents to do intelligent things; that requires intelligent rules.The instinct is to add guardrails around the agent: instructions, policies, and monitoring layered above the model. Those mechanisms matter, but they share a structural limit: The car-door rule is plausible right up until the moment you actually have to decide whether to open the door. Controls at the agent layer are only as reliable as the agent’s output is predictable, and autonomy is precisely the property that makes that output hard to predict. Governance that depends on reviewing an action before it happens cannot keep pace with a system that acts in milliseconds, across many systems at once.Governance has to become executable, and enforced where agents actually do their work: at the operational data layer, in the context, and exactly at the moment it is happening. The data layer is the enforcement pointAgents create value by touching data. They query it, retrieve it, transform it, and increasingly act on it. A policy that says an agent should not reach a certain class of data is meaningful only if the system can deny that access at the moment the agent requests it. Additionally, a principle that says AI must be auditable is meaningful only if the organization can reconstruct what the agent did, what data it touched, which user it acted for, and what resulted. When governance lives at the data layer, it holds regardless of how the agent was built or how it behaves, because the control is a property of the database itself, not a promise made by the agent.Agent behavior may be probabilistic. Governance cannot beThe enterprise should not rely on a model choosing to follow policy. The policy has to be enforced by the system. That is the difference between hoping an actor stays in bounds and constructing bounds it cannot cross to begin with.The controls that make this real are ones many enterprises already run at the data layer: role- and attribute-based access, row- and column-level security, classification and masking, policy as code, and complete audit trails. What agents change is not the mechanism, but who the mechanism has to recognize. Identity management has to treat the agent as a principal in its own right, with its own identity and a purpose declared when the session opens. Once purpose is bound to identity, the policy engine can evaluate it the same way it evaluates role or department today, and the record of what happened can capture not just who acted and what they touched, but what they declared they were there to do.In practice, this resolves into nine controls, grouped under three imperatives:Enforce itRole- and attribute-based access control enforced at query time, for agents as well as usersDynamic column masking driven by the same policy pathAgent identity as a first-class principal, with declared purpose bound at session start and the acting user preservedSee it and prove itClassification and tagging that drives policySession-level audit logging that records which agent acted, for which user, and under what declared purposeLineage across pipelines, so a result can be traced back to the request that produced itUnify and hardenCentralized, portable policy managementEncryption at rest and in transitConsistent enforcement across on-prem, cloud, and sovereign or air-gapped environments“Declared purpose is what makes the difference. It becomes an attribute the access layer already understands, evaluated in the same policy path as role and row-level security. The enforcement mechanism does not change. What changes is that the agent's purpose is part of what it evaluates, and part of what the record proves afterward,” says Priyanka Jain, VP, product management, data & AI governance, EDB. Wherever you are in your AI adoption journey, enforcement at the data layer is what lets you move faster rather than slower. The controls are already in the database. The difference is that agents now have to pass through them.A digital leash, not a locked doorThe goal is not to stop agents from doing useful work. It is to define how far an agent can go, what it can touch, what it can change, what requires escalation, and how the organization can reconstruct events if something goes wrong. Governed this way, agents are identified, scoped, monitored, and auditable. The enterprise can adopt them faster, because security, risk, and leadership teams trust the operating model underneath.Open, sovereign, and enforceable at the sourceBuilt on open source Postgres, this open foundation keeps enterprises in control of where their data lives, who can reach it, and under what policy, without ceding governance to a layer they don’t own or can’t inspect. For regulated industries, that combination of data sovereignty and source-level enforcement isn’t a nice-to-have; it’s the precondition for putting agents into production at all.Agentic systems will keep getting more capable and more autonomous. That is a reason to be deliberate about where control lives, not a reason to slow down. The enterprises that enforce governance at the data layer can move aggressively on AI, because the thing protecting their data is more than just wishful thinking. EDB Postgres AI is an open, enterprise-grade sovereign data and AI platform that unifies transactional, analytical, and AI workloads — with governance enforced where the data lives. For the full framework, see EDB’s white paper Governing Agentic AI at Enterprise Speed.Max Romanenko is Chief Technology Officer at EDB.Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. For more information, contact sales@venturebeat.com.
Biz & IT - Ars Technica
Report shows Meta's challenges replacing people with AI agents.
The Robot Report
Gatik said the funding will help it expand its model built around high-frequency regional routes connecting distribution centers and stores.
The post Gatik brings in $200M to continue expanding autonomous trucking operations appeared first on The Robot Report.
The Robot Report
IDS said Nion is suitable for applications where precise 3D data is required at real-world process speeds, such as logistics and robotics.
The post IDS Imaging adds Nion ToF sensor to its portfolio of 3D cameras appeared first on The Robot Report.
AI | VentureBeat
Presented by Tata Communications Enterprises are deploying AI agents, voice AI, and automation across messaging, voice, and digital channels faster than the architecture meant to support it. Most of that deployment has involved attaching conversational AI to legacy systems never built for it, says Gaurav Anand, global head of the Customer Interaction Suite at Tata Communications."In the rush to deploy AI, organizations have largely bolted conversational AI onto legacy systems," Anand says. "As a result, while many enterprises have adopted digital tools, very few have platforms that are truly integrated, scaled, and capable of seamless orchestration."That gap creates a heavy cognitive load for human agents who must piece together context across disjointed tools to understand what an AI system has already told a customer. The challenge is not simply access to data, but the absence of a shared enterprise context that connects customer identities, interactions, transactions, policies, journeys, and operational systems into a common understanding. Traditional CX architecture was built for linear, human-driven routing, not for managing real-time data flows between autonomous AI systems, data lakes, and human workers."Today's operational complexity is no longer about adding more intelligence," he adds. "It is about coordinating the existing intelligence across the enterprise, so the enterprise customer never feels the friction of those internal silos. That requires a shared context layer that allows AI systems, applications, and people to operate from the same understanding of the customer and the business."Why orchestration is replacing automation as the top CX priorityAs that coordination problem grows, Anand says the strategic priority inside enterprises is shifting from automation to orchestration."Automation solves individual tasks, whereas orchestration connects them into end-to-end outcomes," Anand says. "The next evolution is context-aware orchestration, where AI agents, applications, and human workers operate using a shared understanding of customers, processes, and business intent rather than isolated system records."As organizations accumulate more bots, agents, and AI tools, managing them grows exponentially more complex. Anand says the competitive advantage now sits less in deploying automation and more in how intelligently systems hand off work, collaborate, and escalate. The trap of bolting AI onto legacy systemsCompanies that simply place a voice AI agent in front of an existing system are repeating the same old mistake. Instead of improving the experience, they end up recreating the deterministic phone menus AI was supposed to replace. The real benefit of AI is the scale, speed, and orchestration it provides.Anand points to a wave of consolidation across the industry, as established contact center providers acquire AI-native firms to close capability gaps and strengthen their customer experience offerings. The broader industry shift reflects a growing recognition that enterprises need more than channels and automation; they need an intelligence layer capable of orchestrating AI, people, data, and workflows across the business.The goal across industries is to make AI the connective layer between customers, employees, and enterprise systems. To achieve that, organizations increasingly need a common enterprise ontology: a shared business vocabulary that aligns customer data, products, policies, SOPs, transactions, and workflows across otherwise disconnected platforms.Tata Communications’ solution is the Interaction Fabric, an orchestration layer that unifies contact center, messaging, collaboration, AI, and customer data while coordinating AI agents, channels, and enterprise systems in real time. Underpinning that orchestration is a context-driven architecture that continuously connects identities, conversations, transactions, and operational data so interactions retain continuity across channels and touchpoints.That means AI and agents can move across voice, WhatsApp, chat, email, and CRM workflows without losing customer context. Identity, intent, and AI-driven insight flow continuously across channels instead of remaining trapped in disconnected applications.The next phase of orchestration is not simply coordinating tasks across systems, but coordinating them through a shared understanding of the enterprise. Context graphs, built on enterprise ontologies, create that common understanding by connecting customers, interactions, products, policies, decisions, and outcomes across organizational silos. This allows AI agents and human workers to operate from the same source of context, driving more accurate decisions, seamless handoffs, and consistent customer experiences.But synchronizing customer intent, conversation history, enterprise data, and AI decision-making across channels only works without lag. Legacy networks not designed for modern data frequency create what Anand calls data gravity, producing latency and inconsistent journeys as users switch channels."The underlying network needs to be engineered to be as agile as the AI systems running on top of it," he explains. "Interactions stay synchronous and technology itself becomes invisible, leaving only an experience that feels effortless."Making AI a better partner for human agentsEffective shared visibility between human agents and AI systems starts with the agent experience rather than any single technology. The most effective implementations allow both the AI and human agent to operate from the same contextual understanding of the customer, ensuring that information gathered in one interaction can inform the next regardless of channel or system. Automated call summaries, real-time sentiment analysis, and AI-powered assistance provide agents with instant, actionable insights and suggested next steps directly within their workflow.That allows AI to handle routine, high-volume tasks such as password resets, delivery tracking, and account updates, while human agents focus on interactions requiring judgment and empathy."If a customer is facing a sudden crisis like a fraudulent transaction, the AI can instantly block the card, but it cannot provide the emotional comfort and delicate communication needed in that moment of panic," Anand says. "The answer to the dilemma is intelligent orchestration, rather than a choice between systems." In practice, AI handles the immediate technical transaction, while real-time sentiment analysis recognizes the customer's distress and routes the call to a human expert. The objective is to orchestrate AI and human agents together so efficiency never comes at the cost of brand trust and loyalty.Building a unified CX architectureMoving from fragmented experimentation to coordinated orchestration requires both technical and organizational change, Anand says, beginning with consolidating data and fragmented point solutions onto a unified, cloud-first platform."IT and CX teams need to work more collaboratively," he explains, describing that alignment as the second necessary shift, this time at the organizational level.At the architecture level, Anand says communication APIs need to be embedded into the enterprise's core so every function operates from the same customer context instead of maintaining its own siloed data. Increasingly, this means moving beyond integration alone toward a contextual architecture where a shared ontology and context graph provide a common understanding across CX, operations, sales, service, and AI systems. The deeper organizational change, he says, is a mindset shift from reactive support toward proactive, predictive, and personalized engagement, which he calls the three Ps.How AI agents will shape the future of CXCustomer engagement over the next several years will be defined by real-time intelligence, increasing autonomy, and seamless orchestration across touchpoints, and persistent enterprise context that follows customers, employees, and AI agents wherever interactions occur. Rather than analyzing interactions after the fact, enterprises will increasingly shape conversations in real time. "The future of CX will be defined by simplification, aligning data, infrastructure, and operating models around clear customer outcomes rather than adding more models and tools," Anand says. "The rise of AI-powered agents and agent-to-agent interactions is a defining trend, with AI systems moving beyond assisting humans to independently managing and resolving interactions, creating a largely invisible layer of engagement that improves speed and efficiency." Human agents will increasingly work alongside AI, supported by real-time conversational intelligence and next-best-action recommendations to deliver what Anand calls Total Experience: a unified model that brings together customer, employee, and AI-driven experiences. Tata Communications is building toward that future through its Voice AI, AI Workers, and Total Experience Hub solutions."Ultimately, customer engagement will evolve from being reactive to predictive and increasingly generative," Anand says. "Enterprises won't just be responding to needs, but actively shaping and improving customer journeys in real time."Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. For more information, contact sales@venturebeat.com.
The Robot Report
Bedrock Robotics deploys autonomous excavators to active job sites, tackling construction labor shortages with AI-powered heavy machinery.
The post Bedrock Robotics’ first operator-free excavator deployments take off appeared first on The Robot Report.
Robohub
Deborah Lupton / Servers in a Landscape / Licenced by CC-BY 4.0 By Emmet Cole Robots clean rivers and sort waste, monitor ecosystems, and inspect renewable-energy infrastructure. But even the greenest robot has an environmental footprint. If robotics is going to help build a more sustainable world, the robotics community has to answer some potentially […]
IEEE Spectrum
This article is brought to you by Ollobot.From about 2017, individuals began to truly connect with the initial wave of companion robots. These devices had personality, moved around, joked, and answered when you spoke to them. Most early companion robots, however, were still limited by simple voice-command interactions and narrow functionality. Once the novelty wore off, many ended up sitting unused on shelves. As some of those companies went out of business and turned off their servers, many owners likened it to losing a pet.What Ollobot describes as “gentle intelligence” is a useful way to think about where the serious work in this category is going. Not toward more powerful assistants, but toward more present ones.The problem companion robots were trying to solveLoneliness is not a niche issue. According to one study, nearly one out of three elderly adults resides alone, meaning they do not have daily companions. Research also shows that children whose parents have migrated for work, leaving them in the care of relatives, were 2.5 times more likely to experience loneliness than children whose parents remain with them. Among working adults living alone in urban environments, similar patterns of social isolation emerge, even if they are less visible.Over the years, technology has time and again attempted to solve this problem via video calls, smart speakers, and messaging apps without much success. Those tools are geared towards communication between people that already have relationships. They do not create presence. They schedule it. That is the gap that a new generation of AI companion robots is being engineered to fill.Today’s AI robots are differentToday’s companion robots are not just cute and cuddly. They are designed with psychological research, clinical insight and long-term interaction models to be truly useful in real homes.Three fundamental shifts define the current generation:From reactive to proactive response. Older robots relied on you speaking to them, but modern robots monitor a room with cameras, microphones, and surroundings sensors to initiate interactions without your input, and they can pick up on your emotions.From function-oriented to emotion-oriented design. The original pitch for companion robots was about what they could do. The question driving the serious work now is how they make you feel, which is a harder engineering problem and a more honest framing of what the product is actually for.From standalone hardware to connected ecosystems. Leading brands are creating platforms rather than devices with software included as a built-in layer and remote access from the beginning.The global AI companion market size was valued at US $36.8 billion in 2025 and is projected to grow from $48 billion in 2026 to $318 billion by 2033, at a compound annual growth rate of 31 percent from 2026 to 2033.Three household scenarios and interaction modelsOllobot’s advanced AI family companion robot OlloNi SS1 addresses a number of gaps in what existing technology offers.Elderly individuals living alone. The combination of proactive interaction, fall detection, and persistent presence addresses both safety and companionship without the social overhead of asking family members to check in more frequently.Children in households where parents work far from home. The SS1 functions as a consistent companion that already knows a child, their preferences, their moods, and their routines. The remote connection features allow parents to stay present without requiring a scheduled call, and the life recording system gives them a passive window into their child’s days that feels less clinical than a monitoring camera.Single professionals living alone in cities. The SS1 adapts to daily routines, builds up a preference model over time, and provides ambient social presence without demands. OlloNi SS1 adapts to daily routines over time.OllobotWhat OlloNi SS1 is doing differently?Ollobot’s goal in building intelligent companion robots is to address the gaps in technology and capability, using innovation not to automate tasks but to fill emotional voids.Much of the robotics industry has historically pursued human imitation — machines that speak, look, or behave like people. The SS1 is instead designed around familiarity and long-term coexistence rather than realism.The system integrates multiple subsystems operating in parallel, including visual perception, audio processing, mobility control, and interaction management. It is equipped with a multi-chip AI 4K vision module capable of facial recognition and motion tracking. One small but revealing detail is the inclusion of a physical privacy cover for the camera — a mechanical solution to concerns that software settings alone may not fully resolve. OlloNi SS1 can actively integrate into family activities, and it can autonomously move closer to capture memorable moments or reposition itself to remain engaged in ongoing interactions.OllobotThe robot supports advanced mobility across multiple indoor surfaces, including wooden floors, ceramic tiles, and low-pile carpets, with slope climbing capability up to 3.5 degrees. Rather than remaining in a fixed location, it can move naturally throughout the home to stay close to household members as daily activities unfold. For example, the OlloNi SS1 may greet family members when they arrive home, follow an older adult from the living room to the kitchen while continuing a conversation, remind a child to take a study break after a prolonged period of inactivity, or notice that someone appears unusually quiet and gently check in. During family activities, it can autonomously move closer to capture memorable moments or reposition itself to remain engaged in ongoing interactions.The robot continues to evolve over time, with over-the-air updates that deliver new features, performance improvements, and AI enhancementsIt also incorporates fall detection with optimized accuracy for safety monitoring scenarios. A 6-microphone array enables omnidirectional voice pickup with an effective voice capture range of up to 5 meters, supporting reliable wake-word detection and far-field interaction.To support continuous companionship, much of the robot’s AI processing takes place directly on the device through its “heart module” architecture, with 16 GB of memory and 64 GB of local storage. This enables the system to retain household memories, recognize familiar faces, and respond with lower latency, making interactions feel more natural even during everyday routines.Because companion robots are expected to remain available throughout the day rather than only during brief interactions, the SS1 is designed for extended operation, offering up to 12 hours of standby time and around 5 hours of active interaction on a single charge. This allows it to accompany users through meals, conversations, playtime, and other daily activities without frequent interruptions. To support engaging interactions, much of the robot’s AI processing takes place directly on the device through its “heart module” architecture.OllobotLike the relationships it is designed to build, the robot continues to evolve over time. Running on Android OS with over-the-air (OTA) updates, the system continuously receives new features, performance improvements, and AI enhancements, allowing its capabilities to grow alongside the household it serves.The robot’s behavioral model also improves over time. Rather than reacting to isolated commands, it attempts to establish a baseline understanding of household routines and individuals. Changes in behavior — prolonged quietness, unusual inactivity, or emotional cues — become triggers for interaction.Presence instead of utilitySeveral features in the OlloNi SS1 illustrate this emphasis on presence and continuity in its interactions.The system can identify different household members, including pets, and adapt responses accordingly. Remote communication features allow family members to connect through the device without treating every interaction like a scheduled call. Environmental sensors support contextual reminders tied to weather or room conditions.Its “2+1” multi-display configuration is also designed around emotional communication. Two circular side displays function as expressive “emotional eyes,” while a separate primary display handles information and structured interaction. The separation allows emotional signaling and functional communication to operate independently, creating more intuitive nonverbal interaction even when no dialogue is taking place. The robot’s behavioral model improves over time. Rather than reacting to isolated commands, it attempts to establish a baseline understanding of household routines and individuals.OllobotThe SS1 also includes an automated life-recording system built on facial recognition and behavioral-event detection that can capture moments such as laughter, physical closeness, or group interaction automatically. An integrated AI vlog engine can then organize those moments into edited short-form videos with automated sequencing and soundtrack generation. The design intent is to preserve spontaneous domestic moments without requiring active documentation behavior from users.An integrated AI vlog engine can organize recorded moments into edited short-form videos with automated sequencing and soundtrack generationVisual data is processed primarily on the device through the SS1’s on-device AI architecture, with household memories stored locally and managed within Ollobot’s proprietary ecosystem instead of being shared with third-party smart home platforms. Access to recordings and live feeds is restricted to authorized users through the companion app, while encrypted communication helps protect data during remote access. Users also retain direct control over recording preferences, and the physical camera privacy cover provides an additional hardware-level safeguard whenever visual monitoring is not desired. Learn more at ollobot.com.Remote communication is similarly structured around persistence rather than transaction. Traditional video calls are episodic and screen-bound; the SS1 instead acts as a continuously present interface embedded inside the household environment. Through autonomous mobility, environmental awareness, and persistent household memory, remote family members interact with an ongoing domestic context.The larger shift to “gentle intelligence”Ultimately, gentle intelligence is not about making robots behave more like humans — it is about helping them fit more naturally into human lives. Each OlloNi SS1 unit develops a unique behavioral profile based on its household. Two units running in different homes for a year will have become meaningfully different from each other, shaped by the specific people, habits, and rhythms of where they live.That kind of long-term personalization is what early companion robots never had. It is also what makes the difference between a product that ends up on a shelf and one that actually earns its place in a home.Learn more at ollobot.com.
Biz & IT - Ars Technica
Is the technique outdated? Yes. Is it still creepy? Also yes.
robotics | TechCrunch
General Intuition, the startup building a foundation model that trains generalized AI agents how to move through space and time, is in talks to raise at a $6 billion pre-money valuation from new investors including Valor Ventures, Point72 Ventures, and Seven Seven Six.