Tokyo’s Shin-tomi nursing home uses 20 different robot models to care for its residents. PARO, a fluffy animatronic harp seal that took over a decade to develop and received roughly $20 million in government funding, responds to touch and speech by moving its head, blinking, and playing recordings of seal cries. SoftBank’s Pepper leads afternoon exercise sessions and runs scripted dialogues. Tree, a walking rehabilitation device, crawls along the floor and tells patients where to place their next step in a gentle feminine voice—”right, left, well done!” Panasonic’s robotic bed transforms into a wheelchair. Monitoring systems track falls. The facility has become a showcase that more than 100 foreign delegations visited in a single year, from China, South Korea, the Netherlands, and elsewhere—countries watching Japan navigate a demographic crisis they know is heading their way.
Japan has 36.25 million people aged 65 or older as of 2024, roughly 29 percent of the population. By 2065, one in every 2.6 people in Japan will be 65 or older. The country has the highest life expectancy and the largest proportional elderly population of any nation on earth. The birth rate has been declining for decades. The labor shortage in elder care is acute: as of recent data, there is only one applicant for every 4.25 job openings in the sector. The Ministry of Health, Labour and Welfare projected a shortage of 370,000 caregivers by 2025. Projections for 2040 suggest a shortfall of 11 million workers across all sectors. Japan isn’t building care robots because robots are cool. Japan is building care robots because the math doesn’t work any other way.
What the robots actually do (and don’t do)
The most detailed ethnographic account of care robots in practice comes from a researcher who spent over 18 months in Japanese elder care facilities, including extended observation at a home trialing three robots: Hug (a lifting device), PARO (the seal), and Pepper (the humanoid). The findings are instructive for what they reveal about the distance between the technology’s promise and its operational reality.
Hug, the lifting robot designed to prevent care workers from manually lifting residents, was abandoned within days. Staff found it cumbersome and time-consuming to wheel from room to room—the time spent maneuvering the device cut into the time they had available to actually interact with residents. The robot was solving a physical problem (back strain from lifting) while creating a logistical one (reduced care time per resident), and the staff decided the tradeoff wasn’t worth it.
PARO was received more favorably. Residents responded to the soft, reactive seal in ways that suggested genuine emotional engagement. But complications emerged quickly. One resident kept trying to “skin” PARO by pulling off its synthetic fur. Another developed such an intense attachment that she refused to eat meals or go to bed without the robot by her side. Staff ended up monitoring PARO’s interactions closely rather than being freed from monitoring duties—the opposite of the intended labor-saving effect. PARO didn’t reduce repetitive behavior patterns in residents with severe dementia, which had been one of the primary hoped-for outcomes. And because PARO can’t move independently, staff had to carry it from room to room, adding a task rather than eliminating one.
Pepper’s deployment followed a similar pattern of expectation meeting friction. Instead of freeing a care worker from leading afternoon recreation sessions, Pepper required a care worker to spend time booting it up, wheeling it into position, and managing the session alongside it. Staff found Pepper difficult to set up. It couldn’t respond to voice commands or move independently—functions that SoftBank acknowledged were needed but hadn’t yet been implemented. Pepper with the Care Prevention Gymnastics Exercises application could facilitate a 40-minute exercise program, but a care worker still had to be present throughout.
The pattern across all three robots is consistent: each one solved a narrow technical problem while creating new operational burdens that partially or fully offset the labor savings. The lifting robot was too cumbersome. The therapeutic seal required more supervision, not less. The humanoid exercise leader needed a human assistant.
Why this keeps happening
The disconnect between care robot demonstrations and care robot reality has a structural explanation. Care is not a logistics problem. It’s a relational activity that happens between people, and the parts of care that are most labor-intensive—emotional engagement, judgment under uncertainty, adapting to a resident’s changing mood and condition—are precisely the parts that robots are worst at.
The tasks robots handle well are the ones that were never the primary bottleneck: leading a scripted exercise routine, playing pre-recorded sounds, transforming a bed into a wheelchair. The tasks that consume the most caregiver time and cause the most burnout—managing behavioral complications of dementia, providing emotional support to residents who are frightened or confused, making real-time clinical judgments about a resident’s condition—require exactly the kind of contextual, adaptive, emotionally intelligent interaction that current robotics can’t deliver.
Some researchers have raised ethical concerns that cut deeper than efficacy. Using toy-like robots with dementia patients raises questions about infantilization—treating cognitively impaired adults as if they were children comforted by stuffed animals. PARO’s therapeutic seal form is specifically designed to trigger nurturing responses, but residents with cognitive impairment may believe it’s a real animal, raising the question of whether therapeutic benefit achieved through deception is acceptable. Care workers in multiple studies have expressed discomfort with this dynamic. Others have raised privacy concerns about robots with cameras and microphones operating in residents’ living spaces, with some staff reporting the feeling that the robots were monitoring their work.
The demographic argument doesn’t go away
None of these complications change the underlying math. Japan will have one person aged 65 or older for every 2.6 people in the total population by 2065. The caregiver shortage isn’t projected to close through immigration—as of 2017, only 18 foreigners held Japan’s nursing care visa. The Specified Skilled Worker System introduced in 2019 has expanded foreign labor in care, but the numbers remain far short of the projected deficit. The healthcare and welfare sector is on track to become the largest industry in Japan, and there aren’t enough humans to staff it.
The Ministry of Economy, Trade and Industry estimates the domestic care robot industry will reach ¥400 billion (roughly $2.7 billion) by 2035, when a third of Japan’s population will be 65 or older. The global market in 2016 was $19.2 million. The growth curve is steep because the need is not optional—it’s demographic arithmetic.
The robots that are gaining traction are not the charismatic humanoids that generate media coverage. They’re the unglamorous operational tools: monitoring systems that detect falls and irregular behavior, robotic beds that reduce transfer injuries, sensor networks that track resident movement patterns and alert staff to anomalies, telepresence systems that allow remote caregivers to check on residents without being physically present. These don’t make for compelling photographs, but they address real workflow bottlenecks without requiring residents to form emotional relationships with machines.
What Japan is actually teaching the world
Japan’s two decades of investment in elder care robots have produced a result more valuable than any individual robot: a detailed, field-tested body of evidence about what happens when you deploy technology into a care environment. The lessons are consistent and transferable. Robots that create operational burdens for staff get abandoned regardless of their technical capabilities. Robots that require human supervision don’t reduce labor costs. Therapeutic robots raise ethical questions that the engineering alone can’t answer. And the tasks most in need of automation—the emotionally complex, contextually adaptive, judgment-intensive interactions that define good care—remain beyond current robotic capability.
The countries sending delegations to Shin-tomi aren’t just shopping for robots. They’re studying what happens when a technologically advanced society confronts an irreversible demographic shift and discovers that the hardest problems in elder care aren’t the ones that engineering solves most easily. Germany, China, Italy, South Korea, and eventually the United States will face the same mathematics. Japan got there first—and what it found is that the robots help, but they don’t fix, because what’s breaking isn’t a machine. It’s a labor market, a social contract, and a set of political choices about who takes care of the people who can no longer take care of themselves.
We cover Japan’s elder care robotics alongside the uncanny valley, the humanoid robot race, and the full landscape of human-robot interaction across our Humanoid Robots & Drones course—including why the most expensive robot in a nursing home is often the one nobody uses.