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 solve

Loneliness 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 different

Today’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:

1. 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.
2. 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.
3. 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 models

Ollobot’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.Ollobot

What 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.Ollobot

The 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 enhancements

It 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.Ollobot

Like 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 utility

Several 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.Ollobot

The 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 generation

Visual 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.

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.

Electronic waste is moving up on regulatory agendas in 2026. New European waste-shipment rules, expanded recycling fees on products with non-removable batteries in California, and an e-waste import ban in Malaysia, for example, are all increasing pressure to recover more value before electronics are shredded or exported.

The world is projected to generate 82 million tonnes of e-waste annually by 2030, according to the United Nations’ most recent Global E-Waste Monitor report in 2024. The report estimated that current e-waste management captures less than a third of the recoverable metal value contained in discarded electronics.

For recyclers, much of that lost value is a consequence of what happens before a circuit board ever reaches a smelter or shredder. Boards contain a mixture of components such as memory chips, processors, magnets, and capacitors, as well as valuable raw materials such as copper, aluminum, tantalum, and precious metals. Conventional recycling often mixes everything into bulk streams and destroys components that might otherwise be reused.

Tuurny, a startup based in San Francisco, is developing an automated system to remove and separate reusable chips from circuit boards before the remaining material is shredded. In April, the company announced it had designed a robotic system, called Nantul, to identify and extract RAM integrated circuits, claiming each machine can recover 300 intact RAM ICs per hour.

Sina Ghashghaei, Tuurny’s founder, says the company is preparing its first field deployment with dozens of machines through a six-figure deal with Areera, a television recycler in the United Kingdom, which processes 1,500 tonnes of televisions per month. The deployment is planned for early 2027.

Tuurny’s first target is recovering RAM ICs and other chips used in legacy systems where replacement components can be difficult to source. Ghashghaei says the company is talking with a few legacy chip suppliers and pursuing potential agreements to supply aluminum and copper recovered from circuit boards to smelters and refiners. He declined to identify the companies involved.

Robots for Automated RAM Recovery

Traditional electronics recycling often begins by shredding boards and sorting the mixed output afterward. Tuurny aims to do the opposite: Identify and remove components first, sort them by model or material, then reroute the recovered items to testing labs for potential reuse as new chips or to refiners and smelters for further processing.

Nantul comprises three robotic systems in one. The first is an arm to continuously feed the component-removal robots, paired with two tabletop machines similar to 3D printers or computer numerical control (CNC) machines. A neural network identifies and catalogs components, then searches the internet for manufacturers’ thermal-profile specifications. Nantul uses those specifications to employ a combination of suction, controlled heat, computer vision, and robotic controls to remove chips while minimizing damage. Recovered items are then sorted by model number in material-specific groups.

“We’re creating a new supply chain from old feedstock that didn’t exist before,” Ghashghaei says, adding that manual recovery is expensive and difficult to scale.

Tuurny’s recovery system includes a computer vision system that identifies specific RAM components to assess them for recovery.Tuurny

Minghui Zheng, an associate professor of mechanical engineering at Texas A&M University, in College Station, who studies robotic disassembly and electronics recycling systems, says Tuurny’s approach appears technically feasible, especially when focused on the narrow, valuable target of recovering RAM from more controlled e-waste streams.

“RAM is a good starting point because it has relatively high reuse value and is more standardized than many other electronic parts,” Zheng says. The harder challenge, however, is removing chips “without heat, mechanical, or electrical damage, and making sure it still works reliably afterward.”

Used circuit boards can vary by layout, markings, age, contamination, solder condition, or prior damage. A robot has to identify the correct component, choose a removal strategy, apply heat locally, lift the part cleanly, and preserve enough information about the part for downstream testing and resale.

E-Waste Recycling Strategies

Ghashghaei says Tuurny is building small modular machines using off-the-shelf parts, custom controls, and Nvidia Jetson Nano hardware. The company is trying to keep costs down by reducing hardware complexity to arrive at a price point far below centralized industrial equipment used at large facilities. He says the biggest challenge from an engineering perspective has been developing the autonomous computer vision and robotic control.

Last year, the four-person startup received a NASA-funded grant to support an AI-powered repair assistant for printed circuit boards that used computer vision and a custom large language model (LLM) to guide technicians.

Ghashghaei says Tuurny pivoted from board repair to e-waste processing after concluding that discarded electronics represented a larger market amid growing interest in the U.S. around on-shoring capacity for critical minerals and rare earths. The pivot also positions Tuurny to potentially address supply chain concerns around legacy chips for systems in telecom, aerospace, defense, and other industries where equipment remains in service long after chips leave mainstream production.

In practice, Zheng says the main challenge in making robotic disassembly of electronics commercially viable is ensuring it’s adaptable enough to handle the large variability in e-waste while keeping costs reasonable.

“Every electronic product is different, and used boards may be damaged, dirty, or arranged differently. The robot must be able to find the right parts, remove them carefully, and avoid damaging them in real time, which creates major challenges for robotic perception, decision-making, planning, and manipulation,” Zheng says. “Economically, the recovered parts should be valuable enough to justify the costs of the robot, sensing, testing, maintenance, labor, and scaling up the process.”

For smelters and refiners, the question may be whether Tuurny can supply predictable material streams at commercial volumes. Ghashghaei acknowledged that Tuurny’s scaling efforts could run into its own supply chain constraints in trying to acquire enough components to build more robots.

Zheng called Tuurny’s approach promising but still early. “For now, it is more realistic as a targeted recovery strategy for valuable components like RAM,” Zheng says. “The key question is whether the robotic disassembly technology can work reliably, affordably, and at scale.”