ESSEX JUNCTION, Vt. (Aging Untold) — For 10 days, the Champlain Valley Fair, a county fair in Vermont, becomes its own little town with thousands of people, hot afternoons and the occasional emergency.

Charlene Phelps, 74, runs the fair’s emergency response team.

“We have a lot of seniors that come and people don’t drink enough water,” Phelps said.

The team handles sprains, bee stings, heat exhaustion and whatever comes through.

“I like taking care of people, I like helping people,” Phelps said.

Living out a childhood dream

It’s also a childhood dream.

Phelps wanted to be a nurse, but college wasn’t possible, so she found another route into care and has been showing up year after year at the fair.

Aging Untold expert Amy O’Rourke said living out your purpose can improve mental and spiritual well-being.

“When you tap into that, you’re tapping in on a place that’s a risk, that’s a challenge that inevitably creates growth inside you, gives you confidence so that if you’re in another situation you can build on that,” O’Rourke said. “Or, if you’re in an everyday situation where you’re a little anxious, it’ll help create stabilization in that place as well.”

Saving lives at the fair

Sometimes it’s bigger than a bandage.

“Over on there near the swings way over there is Gustovo, and we saved his life,” Phelps said.

Gustovo had gone into cardiac arrest at the fair a few years ago.

“I mean he was gone,” Phelps said.

Now he’s back and working the rides.

“Came for my hug, Gustovo,” Phelps said.

O’Rourke said stories like this are also why some people keep working past retirement age. Purpose isn’t a number, it’s a role.

“I’ve seen a 92-year-old still working as a nurse’s aid. I’ve seen people in my neighborhood chilling out and loving it,” O’Rourke said. “So, I think it’s being really self-aware of what you need and making sure that you’re getting those needs met.”

It was one of the worst commutes in years. A power outage stranded more than 3,500 New York City subway riders in stuffy, crowded train cars for more than two hours on Dec. 11, 2024, during the evening rush.

Firefighters evacuated riders from the disabled trains, but not before some passengers were forced to relieve themselves between cars, according to people who were present. The ensuing delays, which affected the A, C, F and G lines in Brooklyn, stretched well into the morning, snarling the commute for thousands more riders.

But the foul-up didn’t start on the tracks — it began about 40 feet beneath the sidewalk, in a concrete bunker called a substation, like this one.

The Metropolitan Transportation Authority, which runs the New York City subway, operates 225 of these substations. They provide the electricity that keeps trains moving.

Some are deep underground, while others are in fortresslike buildings close to train tracks. Dozens of the facilities are nearing 100 years old, and some components have gone decades without substantial upgrades.

The electrical outage in 2024 started after a critical failure in a Downtown Brooklyn substation that dates to the 1930s. Heavy rainfall most likely seeped into equipment and caused an explosion so forceful that it knocked a door off its hinges, according to the M.T.A.

Without adequate electricity, trains that were closest to the damaged substation could not move, and their ventilation systems shut down.

Such major failures are rare, but are responsible for some of the subway’s worst logjams, said Jamie Torres-Springer, the head of the authority’s construction and development division.

“That’s what causes the most difficult, painful disruptions in the system that drive people out of their minds,” he said.

In hopes of preventing the next nightmare commute, the M.T.A. is making the biggest investment in power in its history. Transit officials plan to spend $4 billion on new power systems by 2029, including upgrades to 75 subway substations. That’s three times as many as were renovated during the last major round of repairs, which ended in 2024.

They have their work cut out for them.

Hidden beneath a steel-trap door on the Upper West Side of Manhattan, 36 steps below the surface, is one of the system’s oldest remaining substations.

“This is a blast from the past,” said David Jacobs, the M.T.A.’s acting general superintendent for power stations, who donned a hard hat and safety glasses on a recent weekday before disappearing into the underground space.

The substation, near 73rd Street and Central Park West, was built in the 1930s, and is expected to be renovated during the current blitz.

A dirty tarp hung in one corner of the cavernous room, to catch water that seeped through worn concrete. Rows of machines hummed with the constant surge of power feeding the electrified third rail on nearby tracks.

It takes about 2 billion kilowatt-hours of electricity to run the subway system annually. That’s enough power to light 128,000 homes for a year.

The substations’ main function is to convert raw, high-voltage electricity from the electrical grid into lower-voltage power that can be delivered to the third rail.

But the aging equipment has become progressively less efficient and reliable, and harder to maintain.

The substations are spaced out across the city, to help keep electricity flowing to trains even if one of them malfunctions. But the equipment has sometimes failed when asked to carry an extra load, leading to cascading problems.

Last year, there were 758 “major incidents” on the subway, ones in which 50 or more trains were delayed. Substations cause a small but disruptive share of the problems, according to M.T.A. data.

“Power is everything,” said John Ross, a recently retired transit worker who was dispatched to help after several service disruptions in the subway, including the outage in 2024. “When it breaks, it breaks good.”

M.T.A. officials assessed the condition of every substation in recent years, and found that 36 percent of the equipment was in poor condition or in need of replacement.

While the main purpose of the upgrades is to reduce train delays, the changes have other benefits. The M.T.A. is installing a new signal system that relies on wireless technology to automatically control train movement.

The system, known as Communications-Based Train Control, or C.B.T.C., will allow trains to operate more reliably. It will also enable transit workers to monitor train traffic more closely from a dedicated room in Midtown Manhattan, known as the operations control center.

But switching to that signal system requires upgrading the rest of the subway’s archaic equipment. “In order to run more trains, we need more power,” Mr. Torres-Springer said.

For Mr. Jacobs, 36, who joined the M.T.A. nearly two decades ago as an electrical apprentice, working with machines younger than him would be a welcome change.

Today he runs a department of almost 400 people, and much of the work remains hands-on: diagnosing problems in the machinery by reading small flags with numbered codes, searching for replacement parts that are no longer manufactured, and generally eking out more life from obsolete machines.

“I do love this equipment,” he said with a smile.

But he’s ready for an upgrade to something built in this century.

“It’s like a B.M.W. versus a 1940 Cadillac.”

For an industrial robot built for the rigors of factories and power plants, tidying up a living room may seem like a light day at the office for Spot. Yet, a recent video of the robot picking up shoes and soda cans in a residential home represents the promise of AI models in robotics. In this case, Google’s visual-language model (VLM) Gemini Robotics-ER 1.5 was empowering Spot with embodied reasoning.

This particular demo grew out of a 2025 hackathon at Boston Dynamics that built on prior projects using Large Language Models (LLMs) and Visual Foundation Models (VFMs) to enable Spot to contextualize its environment and engage in more complex autonomous actions than a typical Autowalk mission. Rather than write formal software logic or a “state machine” program that defines each step of a given task, we interacted with Gemini Robotics using conversational language. In turn, it communicated with Spot on our behalf.

A Robust SDK and Natural Language Prompts Save Time

Using Spot’s SDK, we developed a layer that facilitated interaction between Gemini Robotics and Spot’s application programming interface (API). The API normally gives developers access to the robot’s capabilities to create custom applications or behaviors. For example, researchers at Meta have used Spot to test how an AI system could locate and retrieve objects it had never seen before.

Our ability to engage Gemini Robotics using natural language prompts was a huge timesaver, compared to traditional programming. We told Gemini Robotics it had access to a mobile robot equipped with cameras and a robotic arm. It also had a finite set of tools it could use to control the robot. A tool is a lightweight script that performs some internal logic and translates inputs from Gemini Robotics to actual API calls. We limited the actions to navigating between locations, capturing images, identifying objects, grasping them, and placing them somewhere else. 

The extent of our SDK means there are great examples one could leverage to add more access to the API with minimal development.

Giving Gemini Robotics a Baseline

To start we needed to explain to Gemini Robotics what we wanted it to do. We did experience a learning curve when writing these baseline prompts. Simple instructions like “put down an object” or “take a picture” weren’t detailed enough to produce expected behavior. We had to add context in our descriptions as we refined each tool. 

A good example is the detailed prompt for the “TakePicture” tool:

This command will cause the robot to take a picture with the specified camera. There is some nuance to choosing the correct camera. Once arriving at a location using GoTo, you should always start by taking a picture with the gripper camera, because it's the most informative.
If the robot has arrived at location and is already holding an object, you can do one of two things:
1. Immediately call PutDown
2. Search the area with either of the front cameras. The front cameras are low to the ground, so if you're trying to put things on an elevated surface, they won't give you useful information.

In this example, we gave Gemini Robotics no detailed description of the robot’s chassis or arm. Instead, we simply explained that Spot’s front cameras would be too low to photograph objects on elevated surfaces. We were able to iterate rapidly, as small changes in wording produced noticeably better results. Once it had this set of basic tools through the API, Gemini Robotics could sequence Spot’s actions and follow the handwritten instructions on a whiteboard on the day of the demonstration.

How Gemini Robotics and Spot Collaborate

Until the robot powers on, Gemini Robotics has no context for what specific tasks we might ask it to perform in a given demo. We only provided simple written instructions, such as, “Make sure all of the shoes at the front door are on the shoe rack.” Gemini Robotics evaluated images from Spot’s cameras and identified objects in the scene that matched the instructions. These objects became the reference points for Spot’s navigational and manipulation systems.

In many respects, Gemini Robotics was identical to an operator manually driving Spot using its tablet controller. For example, to pick up an object with Spot, an operator positions the robot near the object and then uses a grasp wizard to identify the target object. The operator provides high-level direction and Spot figures out the exact details. In this demonstration, Gemini Robotics functioned as both the operator and the tablet sending commands to the robot. This freed us up to act more like a team lead, providing a high-level to-do list and trusting Spot and Gemini Robotics do the rest.

Call and Response

When Gemini Robotics engages a given tool, the tool responds with results and context, such as, “I picked up the object,” or “I can’t pick up something while my hand is full.” Gemini Robotics then makes adjustments on the fly based on this feedback from Spot. For example, to pick up shoes, Gemini Robotics requests an image, identifies the shoes in that image, and calls the “pickup” command. By creating fundamental tools that semantically flow in conversation,  Gemini Robotics can manage the sequence of tasks required to clean up the room. Spot’s existing software stack manages the locomotion, navigation, and manipulation of the robot itself.

It’s important to note Gemini Robotics has strict boundaries in this scenario. It can’t invent new capabilities or control Spot beyond what is available through the API. This keeps Spot’s behavior predictable, while still allowing Gemini Robotics to adapt to different situations.

A Force Multiplier for Developers

For developers already working with Spot, this research has tremendous potential. Through Spot’s SDK, they have access to a robust toolkit of capabilities. Companies use these tools today to build applications for inspection, research, and industrial data analysis, among others.

An AI model like Gemini Robotics offers a way to expand those applications more rapidly. Rather than write extensive task logic on top of Spot’s APIs, developers can experiment with having AI systems interpret natural language instructions and dynamically choose to engage the robot. As a result, models like Gemini Robotics can act as force multipliers, amplifying the reliable toolkit and robust performance that is already delivering value for Boston Dynamics customers.

Our Next-Token Prediction for Spot and Gemini Robotics

Although this is still an experimental step and not a hardened application, it illustrates a compelling direction for robotics and physical AI. Robots like Spot are already extremely capable of navigating complex and changeable environments, collecting data and sensor readings, and manipulating objects. Rather than reinventing the wheel, AI foundation models offer a new way to expand these capabilities in new settings and to new applications.

Physical AI is a rapidly evolving field and our team is leading the way in the lab and in real applications of AI empowered robots. While we are early in our formal partnership with Google Deepmind, we’re excited for what the future holds with Atlas and we’ve already rolled out practical enhancements for Spot and Orbit, with AIVI-Learning powered by Google Gemini Robotics ER 1.6. This next evolution of our AI Visual Inspection tool unlocks a new level of visual intelligence, as users benefit from shared expertise bringing a deeper level of contextual intelligence to Spot and Orbit. Model improvements automatically happen behind the scenes, adding more capabilities to the same software and hardware.

Today, this demo points to a future where users can rely more on natural language to guide Spot’s actions, rather than complex code. The engineer’s role shifts toward setting goals and objectives. The multi-modal robot foundation model interprets the instructions to form complex and adaptive plans and Spot executes the action.

This article was contributed by Issac Ross and Nikhil Devraj, engineers on the Spot team.