In 2014, this page contained one link.

It pointed to Vijay Kumar’s famous TED talk, Robots that fly … and cooperate, first presented in 2012. The small quadrotors in that talk could fly through tight spaces, coordinate with one another, form teams and carry out tasks that looked almost choreographed.

The original video is still worth watching: Vijay Kumar — Robots that fly … and cooperate.

But the interesting part in 2026 is no longer that small robots can fly.

We have had drones for years.

The deeper question is:

How do many imperfect machines cooperate well enough that the group can do something no single machine could do alone?

That question turns a spectacular flying demonstration into a lesson about systems.

It is a lesson about sensing.

Control.

Communication.

Local rules.

Shared tasks.

Failure.

Redundancy.

And the surprising fact that intelligence can be distributed across a group rather than concentrated in one central controller.

The original 2012 idea was already bigger than drones

Kumar’s quadrotors were compelling because they were small, agile and autonomous. But the demonstrations also showed another idea: the machines could sense nearby vehicles and coordinate their movement.

They could form ad hoc teams.

They could cooperate on tasks.

The TED description highlighted possibilities such as construction and disaster surveying. Later work across aerial robotics developed the same direction further: teams of autonomous robots mapping unknown spaces, inspecting infrastructure and coordinating physical work.

ETH Zurich’s Autonomous Systems Lab, for example, describes work on cooperative aerial robotic workers for infrastructure inspection and maintenance, while earlier sFly research explored coordinated micro-helicopters for navigation and 3D mapping in urban-like environments. Current research has moved further into learning, resilience and safe cooperation at scale.

Useful current routes include ETH Zurich — Flying Robots, ETH Zurich — SwarmMind and Penn Engineering — Swarm AI safety at scale.

One robot is a machine. A swarm is a network.

A single drone has a relatively clear problem.

• Where am I?
• Where am I going?
• What obstacles are nearby?
• How much battery remains?
• How should the motors change to maintain stable flight?

Add a second robot and the problem changes.

Now each robot must also account for another moving agent.

Add fifty robots and the problem is no longer simply fifty copies of the single-robot problem.

The robots form a network.

Information has to move.

Conflicts have to be resolved.

Tasks have to be divided.

Failures have to be contained.

And the group must avoid becoming so dependent on central coordination that one broken link collapses everything.

Cooperation is not fifty robots obeying one command. Cooperation is a rule system that lets useful global behaviour emerge from many local decisions.

The first hidden problem is localisation

A flying robot cannot cooperate intelligently if it does not know where it is.

Outdoors, satellite navigation can help. Indoors, underground, between tall structures or in damaged environments, GPS may be weak or unavailable.

The robot then has to build position from other evidence.

• cameras;
• inertial sensors;
• laser or depth measurements;
• known landmarks;
• relative positions of other robots; and
• maps built while moving.

This is why autonomous flight is a perception problem as much as a propulsion problem.

A motor can produce thrust perfectly and still fly the robot into the wrong place if the machine’s estimate of reality is wrong.

Then comes control: knowing where you are is not the same as staying there

A quadrotor is continuously unstable in the ordinary sense of the word.

It remains airborne because the control system repeatedly measures its state and adjusts motor speeds.

This creates a fast feedback loop:

sense → estimate → compare → correct → sense again.

That loop happens again and again while the vehicle flies.

Cooperation adds another loop on top of it.

The robot is not only correcting its own orientation.

It may also be adjusting position relative to neighbours, maintaining formation, avoiding collisions, sharing a payload or moving toward a common objective.

Local rules can create global order

This is one of the most beautiful ideas in swarm systems.

A robot does not always need a complete global map of every other robot.

It can sometimes follow simple local rules.

• Do not get too close to a neighbour.
• Do not drift too far away.
• Match a useful direction or velocity.
• Move toward the task region.
• Share new information when it matters.

If the rules are designed well, the whole group can form patterns, distribute itself across an area or maintain coordinated movement.

Nature offers familiar examples.

Birds flock.

Fish school.

Ants allocate labour.

Bees collectively search and communicate.

No single animal necessarily contains a complete plan for the full group pattern.

Order can emerge from interactions.

Cooperation needs communication—but communication has a cost

If every robot constantly broadcasts every sensor reading to every other robot, the network can become overloaded.

Communication consumes bandwidth.

It consumes energy.

Messages can be delayed.

Connections can disappear.

Information can become stale while it is travelling.

So a strong multi-robot system asks:

• What information does this robot actually need?
• Which neighbours need to receive it?
• How fresh must the information be?
• What happens when communication drops?
• Can the robot continue safely with partial knowledge?

This is an information-design problem.

The group becomes powerful when tasks can be divided

One robot can inspect one location.

Several robots can inspect several locations at the same time.

A team can also specialise.

• One robot maps.
• Another relays communication.
• Another carries a tool.
• Another monitors a hazardous zone.
• Another returns to recharge.

Task allocation is where cooperation becomes economically interesting.

The group can distribute work according to position, battery state, sensor capability, payload and urgency.

The problem begins to look less like piloting and more like logistics.

A swarm can also be more resilient than one machine

If a mission depends on one aircraft, the failure of that aircraft can end the mission.

If a mission is distributed across many robots, some failures can be absorbed.

Another robot may take over a region.

A route may be recomputed.

Work may be redistributed.

The mission can degrade gracefully instead of stopping completely.

But redundancy is not automatic.

If every robot depends on one central server, one central map or one central communication channel, the swarm can still have a single point of failure.

Many robots do not guarantee resilience. The architecture determines whether the group can survive loss.

The dangerous side of cooperation is correlated failure

Cooperation creates benefits because machines share information and rules.

The same sharing can create shared mistakes.

If one bad position estimate is propagated through the group, several robots may react incorrectly.

If one learned model contains a failure and that model is distributed to neighbours, the mistake can travel.

If all robots use the same vulnerable localisation or communication method, one disturbance may affect many at once.

Current 2026 research explicitly studies this problem. ETH Zurich has projects examining how autonomous multi-agent systems respond when localisation or communication becomes unreliable. Penn researchers are also studying safety as teams of physical AI agents cooperate and compete at scale.

The engineering question has evolved from “can they cooperate?” to “can they cooperate safely when the world is imperfect?”

Swarm intelligence changes the meaning of learning from failure

A conventional robot can fail, be inspected, receive a software update and try again.

A cooperative learning system raises a more interesting possibility.

One robot fails.

It extracts information from the failure.

The lesson is shared.

Other robots avoid repeating the same mistake.

ETH Zurich’s 2026 SwarmMind project describes this kind of decentralised collective learning: drones learn locally from deployment experience and share model updates with nearby robots.

The swarm therefore begins to resemble a community with accumulated experience.

But that creates another safety question:

When should a machine trust a lesson learned by another machine?

Cooperative robots are useful because the physical world is uneven

A factory floor can be designed around robots.

The outside world cannot.

Buildings have awkward geometry.

Disaster sites contain debris.

Forests block line of sight.

Fires create heat and smoke.

Infrastructure is spread over kilometres.

Some spaces are too dangerous for people to enter immediately.

This is where mobile robot teams become valuable. Different machines can take different routes, share maps and keep humans farther from immediate hazards.

Search and rescue is a natural swarm problem

After a disaster, time matters and the environment may be partially unknown.

A robot team can theoretically distribute the search.

• Map different sectors in parallel.
• Relay imagery to responders.
• Maintain communication between difficult locations.
• Identify blocked routes.
• Search areas before people enter.
• Reassign work when a robot fails or a region is cleared.

This is exactly the type of application that made cooperative flying robots compelling in the first place.

Infrastructure inspection turns a dangerous human task into a routing problem

Bridges, towers, facades and industrial structures are expensive to inspect because people must reach difficult places safely.

Aerial robots can move the sensor toward the structure instead.

Cooperative robots add another layer: several machines can divide the structure, share a map, carry different sensors or help manage tools and tethers.

ETH Zurich’s AEROWORKS work explicitly explored teams of collaborative aerial robotic workers for inspection and maintenance.

In 2026, ETH’s CORD project offers another striking example of cooperation: two tethered drones are used conceptually for high-rise firefighting, with one drone helping manage the cable while another carries the hose and camera.

The lesson is simple:

Sometimes a second robot is valuable not because it duplicates the first robot, but because it removes a constraint the first robot cannot solve alone.

The engineering trade-off: autonomy versus supervision

A fully autonomous swarm sounds attractive because fewer human operators are required.

But the physical world contains edge cases.

Humans still matter for mission intent, safety boundaries, exception handling and accountability.

So many practical systems sit between two extremes.

• Manual control: a human directly controls the vehicle.
• Assisted control: the robot stabilises itself while a human chooses direction.
• Supervised autonomy: the robot plans and acts within constraints while a human monitors.
• Cooperative autonomy: several robots allocate work and coordinate locally while humans supervise the mission.

The most advanced technology is not always the most appropriate operating mode.

Safety depends on choosing the right level of autonomy for the task.

Dual use: the same cooperation can serve very different purposes

Aerial robotics is a classic dual-use technology.

The same ideas that help robots map a disaster zone can also be applied in military systems.

That does not make the underlying mathematics or engineering good or bad by itself.

It means engineering education has to include questions of use, authority and responsibility alongside capability.

• Who defines the mission?
• Who is allowed to operate the system?
• What actions require human approval?
• How are failures contained?
• How is responsibility assigned when autonomous systems make decisions?
• What uses should designers refuse?

A technically successful swarm can still be a socially unacceptable system if these questions are ignored.

What students can learn from flying robots

This single topic connects many school subjects.

• Physics: forces, torque, acceleration, stability and energy.
• Mathematics: vectors, coordinate systems, optimisation, geometry and probability.
• Computer Science: algorithms, state estimation, networking and distributed computation.
• AI: perception, planning, learning and decision-making under uncertainty.
• Engineering: sensors, actuators, control loops, power, materials and reliability.
• Biology: collective behaviour in social insects, birds and fish.
• Social Studies: regulation, public safety, dual use and accountability.
• English: explaining a system clearly enough that another person can follow cause and effect.

The robots are interesting because they make abstract ideas visible.

A simple classroom thought experiment

Imagine one hundred small flying robots must search a damaged building.

Design the rules.

• How do they divide the building?
• How do they avoid searching the same room repeatedly?
• How do they avoid collisions?
• How do they share discoveries?
• What if communication disappears?
• What if ten robots lose power?
• What if one robot reports the wrong location?
• What information must reach the human rescue team immediately?
• Which decisions can remain local?

That thought experiment is already a lesson in distributed systems.

2012 → 2026: what changed?

Period	Visible question	Deeper systems question
2012	Can tiny quadrotors fly autonomously and cooperate?	Can local sensing and control create coordinated group behaviour?
2014	Can students see these robots as a glimpse of the future?	How does cooperation change what machines can do?
2015+	Can aerial robots map, inspect and support agriculture or infrastructure?	How should tasks be allocated across mobile autonomous agents?
2026	Can large teams learn and act together?	How do we make cooperation resilient, safe, accountable and robust to bad information?

The spectacle changed less than the engineering questions.

We were impressed by synchronised flight.

Now we have to ask what happens when the synchronisation is stressed.

Failure is where the system reveals itself

A perfect demonstration tells us what the system can do under favourable conditions.

A failure tells us how the system is built.

• If one robot fails, does the mission stop?
• If one message is delayed, does the formation become unstable?
• If localisation becomes noisy, do robots separate safely?
• If the environment changes, can routes be replanned?
• If the learned model is wrong, can the system refuse or recover?

This is why modern robotics research spends so much effort on robustness and resilience.

The receiver changes what “good cooperation” means

Different people judge the same swarm differently.

• A researcher values a new algorithm.
• A firefighter values reliability in smoke, heat and uncertainty.
• An infrastructure owner values reduced inspection cost and risk.
• A regulator values safety and accountability.
• A member of the public values privacy and appropriate use.
• A student values the chance to see mathematics become physical behaviour.

There is no single performance metric that captures all of these receivers.

A system can be fast but unsafe.

Efficient but fragile.

Autonomous but difficult to supervise.

Accurate but too expensive.

Good engineering balances the operating envelope.

So what does it really mean for robots to cooperate?

It means more than flying in formation.

Cooperation means that information, responsibility and action are distributed across machines.

Each robot has only a partial view.

Each robot has limited energy.

Each robot can fail.

Yet the group can become more capable because the limits are shared.

The swarm works when no single robot needs to contain the whole world, but the system still preserves enough shared truth to act together.

That is why the 2012 video still matters.

It was never only about little helicopters playing together.

It was a visible demonstration of a much larger idea:

simple machines can become a sophisticated collective when sensing, rules, communication and feedback are designed well.

And in 2026, the frontier is no longer just making the collective impressive.

It is making the collective trustworthy.

Sources and further exploration

• TED — Vijay Kumar: Robots that fly … and cooperate
• ETH Zurich Autonomous Systems Lab — Flying Robots
• ETH Zurich — Distributed Flight Array
• ETH Zurich — SwarmMind
• University of Pennsylvania — Swarm AI safety at scale
• ETH Zurich — CORD cooperative tethered drones

Historical note: this eduKate page was first published in July 2014 as a direct link to Vijay Kumar’s TED talk. It was rebuilt in 2026 to preserve the original idea while explaining how cooperative aerial robotics evolved into a broader problem of distributed intelligence, resilience and safe multi-agent systems.