DEV Community: Drift

Simulating Autonomous Drone Flight With the Skydio X2 in MuJoCo

Aditi Sharma — Mon, 10 Aug 2026 11:12:58 +0000

Robots don't have to stay on the ground. The same simulation tools that build ground robots can put a drone in the air and fly it on its own.

This walkthrough moves into the air, building an autonomous drone flight in MuJoCo with the Skydio X2, a real quadcopter, and letting Drift generate the whole takeoff-to-landing sequence from one prompt. It's the same simulation workflow used for ground robots and manipulators, pointed upward.

Why drone flight belongs in a physics simulator

Flying a real drone to test new behaviour is risky and expensive. A crash can destroy the hardware, and every failed attempt costs time and battery. Simulation removes that cost, so you can try a flight path a hundred times, break it safely, and refine it before anything leaves the ground.

Drones are also a natural fit for a physics engine, because flight is all about dynamics: thrust, gravity, momentum, and how the aircraft responds to its own control inputs. MuJoCo simulates exactly those physics, and it ships with community models for aerial vehicles, which is what makes a realistic drone simulation possible without building the aircraft from scratch.

Meet the Skydio X2 drone

The Skydio X2 is a compact autonomous quadcopter made by the American company Skydio, known for AI-driven flight and 360-degree obstacle avoidance that lets it navigate without GPS. It's used for enterprise inspection, public safety, and defense, exactly the kind of missions where a drone has to fly itself reliably.

For simulation, the useful detail is that there's an official Skydio X2 model in the MuJoCo Menagerie, provided by Skydio itself. That means the flight in this walkthrough runs on a realistic, community-standard model of a real drone, not a rough stand-in.

What we asked Drift to build

After creating a new Drift project, we described the flight in a single prompt: create a MuJoCo simulation where the Skydio X2 autonomously flies through a sequence of predefined waypoints and returns to its starting position.

The prompt describes the mission, take off, hit a series of waypoints, come home, rather than the low-level control math to achieve it. That's the point of task-level prompting: you specify the flight you want and let Drift generate the simulation and control to fly it. New to the simulator? Here's how to install MuJoCo in one prompt first.

Watching the drone fly the waypoint course

We launch the simulation by running the generated Python file, and the flight plays out on its own. The drone lifts off, climbs to its target height, and begins following the programmed path from waypoint to waypoint, then returns to its starting position for landing.

Everything runs autonomously from takeoff to touchdown. There's no manual piloting: the drone holds its altitude, tracks each waypoint in sequence, and comes home without intervention. Watching a clean waypoint flight is the clearest way to confirm the basic control loop works before you add anything harder on top.

Where autonomous drone flight goes from here

A waypoint loop is where autonomous flight begins. Everything more advanced builds on a drone that can already fly a known path reliably, so a clean trajectory like this one is the groundwork for the rest.

From here you can work toward obstacle avoidance, so the drone reroutes around things in its way, visual navigation using its cameras instead of fixed coordinates, and fully autonomous missions that string many behaviors together. With Drift generating the simulation, you can move straight to those problems instead of setting up the aircraft and its controller by hand. Change the waypoints, raise the altitude, or tighten the path and see how the flight holds up. The full walkthrough is in the video above.

FAQ

Can you simulate a drone in MuJoCo?
Yes. MuJoCo simulates the flight dynamics of aerial vehicles, and its community model collection includes drones such as the Skydio X2. That makes it possible to build a realistic autonomous flight simulation, from takeoff through a waypoint path to landing, without modeling the aircraft from scratch.

What is the Skydio X2?
The Skydio X2 is a compact autonomous quadcopter from Skydio, an American drone company. It's known for AI-driven flight and 360-degree obstacle avoidance that let it operate without GPS, and it's used for inspection, public safety, and defense. An official model of it is available in the MuJoCo Menagerie.

What is waypoint navigation for a drone?
Waypoint navigation is flying through a predefined sequence of positions in order. The drone takes off, travels to each waypoint in turn, and can return to its start. It's the foundational form of autonomous flight and the basis for more complex missions.

Why simulate drone flight instead of testing on real hardware?
Testing real drones is costly and risky, since a crash can destroy the aircraft. Simulation lets you develop and fail safely and cheaply, running a flight path many times and refining the control before flying real hardware.

How does Drift build the drone simulation?
Drift takes a single natural-language prompt describing the flight, then generates the MuJoCo simulation and the control needed for the Skydio X2 to take off, follow the waypoints, and land autonomously, so you can launch it and watch the full flight without hand-coding the setup.

How Robots Are Reshaping 5 Everyday Industries

Aditi Sharma — Mon, 10 Aug 2026 11:04:13 +0000

Say "robot" and most people picture a humanoid walking around. The biggest impact of robotics is already here, quietly running the industries you rely on every day.

When you hear the word robot, you probably picture a humanoid walking around. The most important industries changed by robotics look nothing like that. The real impact is already happening behind the scenes, in the factories, warehouses, hospitals, farms, and building sites we depend on. Here are five of them.

1. Manufacturing, where industrial robots got their start

It starts in manufacturing, the industry that has used robots the longest. Robotic arms weld, assemble, paint, and inspect products with a precision and consistency people can't match over a long shift.

This is what keeps factories producing everything from cars to smartphones around the clock. There were about 4.66 million industrial robots operating in factories worldwide in 2024, according to the International Federation of Robotics. The arms doing this work are the industrial cousins of the pick-and-place manipulation you can build in simulation, running the same grasp-and-place fundamentals at high speed and scale.

2. Warehousing and the machines behind fast delivery

Once products leave the factory, another set of robots takes over. In warehouses, autonomous robots move inventory, carry entire shelves to workers, and sort packages toward the right destination.

This is what keeps millions of deliveries moving every single day, and it's one of the fastest-growing areas of robotics. We broke down the different machines involved in how warehouse robots actually work, from mobile robots to picking arms to sorting systems, if you want the full picture.

3. Healthcare, from the operating room to the ward

Robotics is also transforming healthcare, in two quite different ways. Surgical robots help doctors perform delicate procedures with steadier, more precise movements than a human hand alone, often through smaller incisions.

Away from the operating room, rehabilitation robots help patients recover mobility, and hospital service robots ferry supplies and medications around the building. Both reduce the physical workload on medical staff and free them to focus on care, which matters more as healthcare systems stretch to meet demand.

4. Agriculture and the robots working the fields

Out on farms, robots are changing how food is grown. They can monitor crop health from the field, harvest produce, and automate the repetitive, labor-intensive tasks that farms have long struggled to staff.

The payoff is efficiency and sustainability: targeting water and treatment only where crops need it, harvesting at the right moment, and working long hours in conditions that are hard on people. Navigating an open, uneven field is its own challenge, closely related to how robots navigate any unstructured space.

5. Construction and robots on the building site

Even construction, one of the least automated industries, is starting to adopt robotics. Autonomous rovers survey sites and track progress, robotic arms lay bricks, and large-scale 3D printers extrude concrete structures layer by layer.

On a job site full of hazards, that shift makes construction both safer and faster, taking people out of the most dangerous and repetitive work. It's an early frontier, but a fast-moving one.

Why this matters more than the humanoid hype

From building our products to delivering them, supporting our doctors, growing our food, and constructing our cities, robotics is already reshaping the world around us. It just doesn't look like the humanoid in the headlines, at least not yet.

What all five industries share is a common toolkit underneath: perception, navigation, and manipulation, developed and tested in simulation before robots are trusted with real work. As that toolkit improves, robotics reaches further into everyday life, and we're only getting started.

FAQ

Which industries are most affected by robotics?
Manufacturing, warehousing and logistics, healthcare, agriculture, and construction are among the most affected. Manufacturing has used robots the longest, while warehousing is one of the fastest-growing, and healthcare, agriculture, and construction are adopting robotics rapidly.

How is robotics used in manufacturing?
Robotic arms handle welding, assembly, painting, and inspection with high precision and consistency, operating around the clock. This lets factories mass-produce goods from cars to electronics at a scale and reliability that manual labor alone can't sustain.

How are robots used in healthcare?
In two main ways: surgical robots help doctors perform precise, minimally invasive procedures, while service and rehabilitation robots move supplies, support patient recovery, and reduce the physical workload on medical staff.

How is robotics used in agriculture?
Agricultural robots monitor crop health, harvest produce, and automate repetitive field tasks. They improve efficiency and sustainability by targeting resources like water and treatment precisely and by handling labor-intensive work that is hard to staff.

How are these industrial robots developed and tested?
Most are developed and validated in simulation first, where their perception, navigation, and manipulation can be tested safely and cheaply before deployment. Tools like Drift generate the simulated robots and environments used for that testing, while the control software is built on top.

Building a Bimanual Manipulation Scene With ALOHA in MuJoCo

Aditi Sharma — Fri, 07 Aug 2026 17:00:58 +0000

Most everyday tasks need two hands. Packing a lunchbox, opening a jar, folding a shirt. That's why a lot of household robotics starts with two arms.

Most household robots need two arms to handle everyday tasks. A single arm can pick something up, but packing a lunchbox or steadying an object while working on it takes two limbs cooperating. This walkthrough builds a bimanual manipulation scene with the ALOHA robot in MuJoCo, using Drift to generate the whole tabletop setup from one prompt.

Why everyday tasks need two arms

Watch how you pack a lunchbox and you'll notice your hands rarely work in isolation. One holds the box open while the other places food inside. One steadies, the other acts. That coordination between two arms is called bimanual manipulation, and it's what a huge share of real household and workshop tasks actually require.

A single arm hits a wall fast on these jobs. It can't hold and place at the same time, so the moment a task needs one hand to stabilize while the other manipulates, you need a second arm. That's a real step up in difficulty from the single-arm pick-and-place we built earlier, because now the two arms have to coordinate without colliding or working against each other.

Meet the ALOHA dual-arm robot

ALOHA (A Low-cost Open-source Hardware System for Bimanual Teleoperation) is an open-source two-armed robot platform from Stanford, built to make bimanual manipulation research accessible. It pairs two arms over a shared workspace so a robot, or a human teleoperating it, can perform tasks that need both hands.

It's become a standard testbed for two-armed manipulation, and there's an official MuJoCo model of the ALOHA 2 version, which is exactly why it's a natural choice for a simulated bimanual scene. If a robot can learn to coordinate both ALOHA arms in simulation, that skill points straight at practical household tasks.

What we asked Drift to build

After creating a new Drift project, we described the whole scene in a single prompt: generate a workbench with an open lunchbox and food items, and place the ALOHA robot ready for future bimanual manipulation.

The prompt describes the finished setup, a workbench, an open lunchbox, food to pack, and the dual-arm robot positioned to work, rather than listing coordinates for each object. That's the point of task-level prompting: you describe the scene you want and let Drift arrange it. New to the simulator? Here's how to install MuJoCo in one prompt first.

How Drift assembles the tabletop scene

From that single prompt, Drift generates the simulation files and assembles them into a ready-to-run MuJoCo scene. The workbench, the open lunchbox, and the food items are placed on the table, with the ALOHA dual-arm robot positioned alongside them and set up for manipulation.

What would normally be a session of scene authoring, importing the robot, placing the props, and configuring the world, is handled in one step. It's the same task-level scene generation behind building an office environment and a kitchen scene, pointed at a two-armed tabletop task.

What comes after the scene is built

The scene loads with the workbench, lunchbox, and food already in place, alongside the ALOHA dual-arm robot, ready to work. But building the task is only half the challenge.

The harder half is next: teaching both arms to work together to actually pack the lunchbox, one arm holding it steady while the other places each item. That coordination, timing two arms so they cooperate instead of colliding, is where bimanual manipulation gets genuinely difficult, and it's the kind of behavior researchers train on platforms exactly like ALOHA. With the environment generated by Drift, you can move straight to that problem instead of spending your time building the table. The full walkthrough is in the video above.

FAQ

What is bimanual manipulation?
Bimanual manipulation is a robot using two arms together to perform a task, such as one arm holding an object steady while the other works on it. Many everyday tasks, like packing a container or opening a jar, require this two-armed coordination that a single arm can't do.

What is the ALOHA robot?
ALOHA (A Low-cost Open-source Hardware System for Bimanual Teleoperation) is an open-source dual-arm robot platform from Stanford, designed to make two-armed manipulation research affordable. It's widely used to collect demonstrations and train manipulation policies, and the ALOHA 2 version has an official MuJoCo simulation model.

Why is bimanual manipulation harder than using one arm?
Two arms have to coordinate in the same workspace without colliding, and often one arm must stabilize an object while the other manipulates it. That coordination, timing, and shared-space planning make it significantly more complex than single-arm pick-and-place.

How do you build a bimanual scene in MuJoCo?
You define a scene with a work surface, the objects for the task, and a dual-arm robot model, positioned so both arms can reach the workspace. In this walkthrough, Drift generates that entire tabletop scene with the ALOHA robot from a single prompt.

How does Drift build the ALOHA scene?
Drift takes one natural-language prompt describing the workbench, objects, and robot, then generates the simulation files and assembles them into a ready-to-run MuJoCo scene with the ALOHA dual-arm robot in place, so you can launch it and move on to the manipulation task.

Gemini Robotics 2 and Whole-Body Control for Humanoid Robots

Aditi Sharma — Thu, 06 Aug 2026 14:42:13 +0000

Google DeepMind's first robotics model taught robots to manipulate objects on a table. Gemini Robotics 2 teaches them to use their whole body to get there.

Google DeepMind has unveiled Gemini Robotics 2, and it's a notable jump for humanoid robots. The first version taught robots how to manipulate objects on a tabletop. This release extends that to the whole body, so a robot can move through a space and act on it as one continuous behavior. It's built on the vision-language-action approach that's reshaping robotics, now applied to entire humanoids rather than just arms.

Whole-body control, from feet to fingertips

Think about picking up a watering can from a low shelf. You don't just move your arm. You walk over, bend down, reach out, and grasp it, all as one fluid motion. Earlier Gemini Robotics models controlled only a robot's upper body for tabletop work. Gemini Robotics 2 plans that entire sequence, controlling the robot from its feet to its fingertips.

In DeepMind's demo, Apptronik's Apollo 2 humanoid is told to "put the watering can into the green bin on the bottom shelf." The robot walks to the object, picks it up, steps over to the shelves, and places it, coordinating balance, locomotion, and manipulation together. That whole-body coordination is exactly what makes humanoids so hard, and getting it into a single model is the headline advance here.

Five-finger dexterity and where it still falls short

Movement is only part of the story. Once a robot reaches an object, it still has to interact with it, which is the harder half. Gemini Robotics 2 can drive a five-fingered, 22-degree-of-freedom hand (Apollo 2's SharpaWave hand) to do delicate tasks like tying a trash bag, sealing a ziplock bag, and handling a light bulb.

It's worth being honest about the numbers, because they show how hard dexterity remains, which is a theme in why robot hands are so hard. In DeepMind's own reporting, the model unscrewed a bulb successfully about 92% of the time but managed only 36% when screwing one back in, with tying a trash bag around 44% and sealing a bag near 40%. It does noticeably better with simpler two-finger grippers on a Franka Duo, scoring roughly 74% on general pick-and-place and about 90% on precise insertion. Five-finger manipulation is progressing, but it's clearly the frontier.

Longer tasks and multiple robots with Gemini Robotics ER 2

Google also introduced Gemini Robotics ER 2, an embodied reasoning model that acts as the robot's high-level brain. Rather than thinking one step ahead, it plans longer tasks that can run for several minutes and hundreds of decisions, tracks progress through them, and knows when a task starts and ends.

It also enables multi-robot collaboration, letting different types of robots communicate and coordinate on a workflow that a single machine couldn't finish alone, like a multi-step cleanup or organizing a garage. In practice, ER 2 plans and supervises while the vision-language-action model executes the motion, and it can self-correct partway through when a step fails.

On-device models that run without the cloud

DeepMind also released Gemini Robotics On-Device 2, a version optimized to run locally on the robot itself. That matters because many real-world settings can't rely on a constant cloud connection, and network latency can break a delicate, multi-step task.

Running on-device makes the model easier to deploy across different robot platforms, and DeepMind says it can adapt to a completely new robot body with only a few hours of training data. The models have been shown across several embodiments, from the Apollo 2 humanoid to a Franka arm, using the same underlying approach.

What Gemini Robotics 2 gets right and what it doesn't

Gemini Robotics 2 is more than a single model release. It brings movement, manipulation, and planning together in one system: the VLA handles motor control, ER 2 handles reasoning and coordination, and the on-device model handles deployment. DeepMind also paired it with a new safety benchmark, ASIMOV-Agentic, aimed at getting robots to refuse unsafe actions and stop safely when a person is nearby.

The honest picture is a real step forward with clear limits, whole-body tasks and gripper work are solid, five-finger dexterity and movement speed still have a long way to go. But combining all three capabilities in one system moves robots closer to handling real-world tasks from start to finish, which is the whole promise of embodied AI.

FAQ

What is Gemini Robotics 2?
Gemini Robotics 2 is a suite of three AI models from Google DeepMind for controlling robots. It includes a core vision-language-action model for whole-body motor control, Gemini Robotics ER 2 for reasoning and multi-step planning, and Gemini Robotics On-Device 2 for running locally on robot hardware.

What is new in Gemini Robotics 2 compared to the first version?
The original Gemini Robotics controlled a robot's upper body for tabletop manipulation. Gemini Robotics 2 adds whole-body control (walking, crouching, and reaching as one motion), stronger five-finger dexterity, longer multi-step task planning, multi-robot coordination, and on-device operation.

What is Gemini Robotics ER 2?
Gemini Robotics ER 2 is an embodied reasoning model that acts as a robot's high-level brain. It plans multi-minute tasks involving hundreds of decisions, tracks task progress, communicates with people, and coordinates multiple robots working together. It's available in preview on Google AI Studio.

How good is Gemini Robotics 2 at dexterous manipulation?
It's capable but uneven. On five-finger hands it does well at some tasks (around 92% unscrewing a bulb) and poorly at others (about 36% screwing one in). It performs more reliably with two-finger grippers, scoring roughly 74% on pick-and-place. Fine multi-finger dexterity remains the hardest part.

Can you experiment with robotics AI like this in simulation?
Yes. Models like these are developed and validated heavily in simulation before running on real robots. Tools like Drift generate the simulated robots and environments used for that testing, so you can prototype navigation and manipulation behaviours virtually before deploying to hardware.

Building a Kitchen Simulation With the Hello Robot Stretch 3 in MuJoCo

Aditi Sharma — Mon, 03 Aug 2026 10:41:53 +0000

Household robots need more than smart software. They need realistic places to practice, and a kitchen is one of the hardest rooms in the house.

Smart software alone won't get a household robot very far. It also needs realistic environments to learn and operate in, and a home kitchen, full of counters, appliances, and clutter, is one of the most demanding. This walkthrough builds a kitchen simulation with the Hello Robot Stretch 3 in MuJoCo, using Drift to generate the whole scene and drop the robot in, from one prompt.

Why household robots start in the kitchen

A kitchen is a genuinely hard environment for a robot. It's full of surfaces at different heights, tight gaps between counters and islands, objects of every shape, and tasks that mix moving around with reaching and grasping. If a robot can operate in a kitchen, it can handle a lot of the rest of a home.

That's exactly why it's a good place to test in simulation first. A realistic simulated environment lets you iterate on navigation and manipulation safely and cheaply, long before risking a real robot in a real room. Getting the environment right is the foundation everything else builds on.

Meet the Hello Robot Stretch 3

The Hello Robot Stretch 3 is an open-source mobile manipulator built specifically for human spaces like homes. It pairs a compact wheeled base with a distinctive telescoping arm and a gripper, so it can drive up to a counter, extend its arm out over it, and pick something up, all with a footprint small enough to fit through the clutter of a real kitchen.

That combination of mobility and manipulation is what makes it interesting for household robotics, and what makes it worth simulating. To do anything useful, it has to both navigate the room and act on objects in it, the two halves of the problem this scene is set up to test.

What we asked Drift to build

After initializing a new Drift project, we gave it one instruction: create a realistic kitchen environment in MuJoCo and place the Hello Robot Stretch 3 inside it, ready for future navigation and household manipulation tasks.

The prompt describes the end state, a furnished kitchen with the robot positioned and ready, rather than spelling out where each counter and appliance goes. That's the point of working at the task level: you describe the scene you want and let Drift assemble it. New to the simulator? Here's how to install MuJoCo in one prompt first.

How Drift assembles the scene

From that single prompt, Drift generates the simulation files and puts everything together into a ready-to-run MuJoCo scene. The kitchen layout, the objects in it, and the Stretch 3 are placed and wired into one launchable file, positioned so the robot is set up for navigation and manipulation from the start.

What would normally be a long session of scene authoring, placing furniture, importing the robot model, and configuring the world, is handled in one step. It's the same task-level scene generation behind building an office environment, pointed at a harder, more cluttered room.

Driving the Stretch 3 in the simulated kitchen

We launch the simulation and the kitchen loads with the Stretch 3 standing inside it. From here, you can already explore what the robot can do.

You can drive its mobile base around the room, extend the telescoping arm out toward a counter, and open and close the gripper, all inside a realistic household setting. Being able to move the base, arm, and gripper by hand first is how you build a feel for the robot's reach and limits before you write any autonomous behaviour. It's the same reason we explored the Franka Panda arm manually before automating it.

Where a household robot simulation leads next

A loaded kitchen with a working robot in it is the starting point for real household robotics. From here you can develop navigation around the counters, manipulation tasks like opening drawers or picking up items, and eventually full routines that combine both.

Building realistic simulation environments is the first step toward capable household robots, because everything, the navigation, the grasping, the failures, can be worked out in simulation before moving to the real world. Drive the base, extend the arm, try reaching an object, and start building from there. The full walkthrough is in the video above.

FAQ

What is the Hello Robot Stretch 3?
The Stretch 3 is an open-source mobile manipulator from Hello Robot, designed for human environments like homes. It combines a compact wheeled base with a telescoping arm and gripper, letting it both navigate a room and reach and grasp objects, and it supports ROS 2 and Python.

Why simulate a household robot in a kitchen?
A kitchen combines everything hard about home robotics: varied surface heights, tight spaces, many object types, and tasks that mix navigation with manipulation. Testing in a simulated kitchen lets you develop and fail safely and cheaply before running on a real, expensive robot.

How do you build a kitchen simulation in MuJoCo?
You define a scene with the kitchen layout, furniture, and objects, then place a robot model inside it, in a MuJoCo scene file. In this walkthrough, Drift generates the full kitchen and positions the Stretch 3 inside it from a single prompt, producing a ready-to-run scene.

What can you do with the Stretch 3 in simulation?
Once the scene loads, you can drive the mobile base, extend and retract the telescoping arm, and operate the gripper. This lets you explore the robot's reach and behavior, and build toward autonomous navigation and manipulation tasks.

How does Drift build the kitchen scene?
Drift takes a single natural-language prompt describing the environment and robot, then generates the simulation files and assembles them into a ready-to-run MuJoCo scene with the Hello Robot Stretch 3 placed inside, so you can launch and interact with it right away.

How Warehouse Robots Actually Work?

Aditi Sharma — Sat, 01 Aug 2026 14:52:13 +0000

Ever wondered how a company like Amazon processes millions of packages a day? Behind the people is a coordinated team of robots, each doing one job well.

Understanding how warehouse robots work means seeing them as a team: several specialized machines, each handling one stage of the journey from shelf to shipping dock. Amazon crossed one million deployed robots in 2025, roughly one robot for every worker, which is the scale that makes same-day delivery possible. Here's what each type does, and why they only matter together.

Mobile robots that bring the shelves to workers

It starts with autonomous mobile robots. In a traditional warehouse, workers walk miles a day to fetch items. These robots flip that around: instead of people going to the shelves, the robots carry entire shelving units, called pods, directly to a picking station.

That single change saves an enormous amount of time, cuts the walking, and keeps orders flowing continuously. The robots navigate the floor and coordinate through a central system that sequences their routes and stops them colliding, which is the same core problem behind how any robot navigates a shared space. Amazon's Proteus is one example of a fully autonomous version that moves among people rather than in a caged-off zone.

Robotic arms that pick the right item

Once a shelf arrives, the harder problem begins: actually picking the correct item out of a mixed bin. That's the job of robotic arms.

Using cameras and AI, these arms identify a specific product, work out how to grasp it without damaging it, and place it into a bin for the next step. Amazon's Sparrow arm, for instance, uses computer vision and suction to handle a large share of its product catalog. This is genuine pick-and-place manipulation, the same challenge you can build and test in simulation, running at industrial scale and speed.

Sorting systems that route every package

Picking an item is only half the job. It still has to reach the right customer. Automated sorting systems handle that stage.

As packages travel through the facility, sorters scan each one, read its destination, and route it toward the correct truck, chute, or delivery lane. At peak volume this happens thousands of times an hour, far faster and more consistently than manual sorting, and it's what keeps the right box heading to the right doorstep.

Inspection robots that keep inventory accurate

Meanwhile, another group of robots is quietly checking stock. Some move through the aisles scanning shelves, while others monitor inventory levels and flag missing or misplaced items before they turn into a delayed order.

This is the unglamorous layer that keeps the rest honest. If the system thinks an item is in a location it isn't, every downstream step fails, so continuous inspection is what keeps the picking and sorting robots working against accurate data.

Why the robots only work as a system

The interesting part is that none of these robots works alone. Navigation, picking, sorting, and inventory are separate jobs handled by separate machines, but a central coordination layer ties them into one flow. A shelf is delivered, an arm picks from it, a sorter routes the result, and inspection keeps the inventory it all relies on up to date.

That orchestration is the real technology. As a fleet grows from dozens of robots to thousands, the hard problem shifts from any single robot to coordinating the whole fleet, deciding which robot goes where, how they avoid each other, and how work is sequenced so throughput holds through a holiday peak. It's the warehouse-scale version of the sense, think, act loop every robot runs, played out across a whole building.

What warehouse automation actually changes

Warehouse robots don't target one worker's job. What they replace is the thousands of repetitive trips, scans, and movements that happen every hour, the walking, the lifting, the manual sorting, the stock counts. Humans increasingly handle the judgment calls and exceptions while robots absorb the repetition.

That division is what lets a modern fulfillment center deliver millions of orders with speed that manual operations can't match. And nearly all of it, the navigation, the grasping, the coordination, is developed and tested in simulation long before a robot touches a real warehouse floor.

FAQ

How do warehouse robots work?
Warehouse robots divide fulfillment into stages handled by specialized machines. Mobile robots bring shelves to workers, robotic arms pick items using cameras and AI, automated sorters route packages to the right destination, and inspection robots track inventory. A central system coordinates them into one continuous flow.

What types of robots are used in warehouses?
The main types are autonomous mobile robots (which move shelves or totes), robotic picking arms (which grasp individual items), automated sortation systems (which route packages), and inspection or inventory robots (which scan shelves and track stock). Some warehouses are also piloting humanoids.

How many robots does Amazon use?
Amazon passed one million deployed robots across its fulfillment network in 2025, approaching roughly one robot per human worker. Its systems include Proteus mobile robots, Sparrow and Robin picking arms, and Cardinal sortation, among others.

Do warehouse robots replace human workers?
They automate repetitive physical tasks, the trips, lifts, scans, and sorts, rather than replacing entire roles one for one. Workers increasingly focus on exceptions, judgment, and oversight, though the growing scale of automation is reshaping warehouse jobs significantly.

How are warehouse robots developed and tested?
Their navigation, grasping, and coordination are developed largely in simulation, where behaviors can be tested safely and at scale before deployment. Tools like Drift generate the simulated robots and environments used for that testing, while the control and coordination software is built on top.

Stacking Three Cubes With a Franka Panda in MuJoCo

Aditi Sharma — Sat, 01 Aug 2026 08:43:19 +0000

Picking up one cube is a good start. Getting an arm to stack three into a tower that doesn't fall over is a much better test of what it can really do.

Cube stacking is one of the oldest and most useful tests in robotic manipulation. It looks simple, yet it packs in perception, precise grasping, and, above all, placement accuracy that compounds with every block. This walkthrough runs that test on a Franka Panda arm in MuJoCo, using Drift to generate the entire stacking sequence from one prompt instead of hand-coding it.

Why cube stacking is a genuine test of precision

A single pick-and-place is forgiving. If the arm sets a cube down a few millimeters off target, it still lands flat on the table and the task counts as done. Nothing depends on it.

Stacking removes that safety net. Each cube becomes the foundation for the next, so errors don't just occur, they accumulate. Place the first cube slightly crooked and the second sits crooked on top of it. Add a third and a small early error can topple the whole tower. To succeed, every placement has to be accurate enough that the next cube has a stable, level surface to rest on. That is why stacking is a standard benchmark for grasping precision and repeatability, and why it's a clear step up from the single automated pick-and-place we built earlier in this series.

What we asked Drift to build

Starting from the existing Franka Panda scene, we gave Drift one instruction: create a new script that makes the arm autonomously stack three cubes into a tower.

Notice how little we had to specify. There's no list of grasp points, lift heights, or drop coordinates. The prompt describes the goal, a tower of three cubes, and leaves the sequence of operations to Drift. That task-level prompting is the whole point of working this way: you say what you want, not how to move each joint. Starting fresh? Here's how to build the pick-and-place scene with this arm, running in MuJoCo.

How Drift turns one prompt into a stacking sequence

From that single prompt, Drift generates a new script and plans each pick-and-place operation the tower requires. It works out the order to move the cubes, where to grip each one, and where to release it so the stack grows cleanly rather than drifting off-center.

Under the hood, this is three dependent pick-and-place routines chained together, the kind of thing that's tedious and error-prone to hand-tune, since a fix to one placement can throw off the next. Drift produces it as a ready-to-run script, which is the same task-level generation behind automating a single pick-and-place, now extended to a multi-step sequence.

Watching the Franka Panda build the tower

We run the new script and watch the arm work. It picks up each cube, carefully aligns it over the growing stack, and sets it down, one block at a time, until the tower stands complete.

The alignment step is the part worth slowing down to watch. Before each release, the arm lines the cube up over the one beneath it, and that small correction is what keeps the tower stable as it climbs. What looks like the same motion repeated three times is really three precise, dependent placements, each one setting up the next.

Where sequential manipulation shows up in the real world

Stacking three cubes is a small task with a big idea inside it: sequential manipulation, where a robot performs a series of steps and the success of each one depends on the accuracy of those before it.

That pattern is everywhere in practical robotics. Assembling a product, packing a box, palletizing goods, and laying bricks on a construction site are all versions of the same challenge, perform precise operations in the right order, where each step builds on the last. A robot that can reliably stack cubes is demonstrating the core skill those jobs require. It's also a natural next step after giving an arm vision through a camera, since real stacking eventually needs the robot to see where the blocks actually are.

Push the tower higher and see where it breaks

With Drift generating the sequence, you get a working stacking demo in one prompt, which frees you to experiment with the hard parts. Add a fourth and fifth cube and see where the tower starts to wobble. Tighten the spacing, change the cube sizes, or offset the target so the arm has to adapt. Each variation is a quick way to probe how much precision the sequence really has.

Watch the full build in the video above, and if you're setting up from scratch, start with installing MuJoCo and building the pick-and-place scene.

FAQ

What is cube stacking in robotics?
Cube stacking is a benchmark manipulation task where a robot arm grasps several blocks and places them into a stable tower. It's widely used to test grasping precision and repeatability, because each placement has to be accurate enough to support the next block.

Why is stacking harder than a single pick-and-place?
A single pick-and-place tolerates small errors, since the object only needs to land on a surface. Stacking chains placements together, so an error in one cube carries into the next and can topple the tower. Every step has to be precise enough to hold the one above it.

What is sequential manipulation?
Sequential manipulation is performing a series of manipulation steps in a specific order, where the success of each step depends on the previous ones. Stacking, assembly, and packing are common examples, and they demand both precision and reliable repetition.

Why is the Franka Panda used for stacking experiments?
The Franka Emika Panda is a seven-joint collaborative arm widely used in research, with readily available simulation models. Its precision and popularity make it a common choice for pick-and-place and stacking tasks.

How does Drift build the stacking sequence?
Drift takes a single natural-language prompt describing the goal, then generates a script that plans and executes each pick-and-place operation needed to build the tower, so the Franka Panda stacks the cubes autonomously without any hand-coded sequence.

The 5 Most Impressive Robot Dogs Right Now

Aditi Sharma — Wed, 29 Jul 2026 07:20:24 +0000

Robot dogs have gone from research demos to real jobs. Some inspect factories, some cross dangerous terrain, and some are learning to work alongside people.

The legged-robot market topped $1.8 billion in 2025 and is projected to keep climbing, which is why these machines are showing up everywhere from power plants to construction sites. Here are five of the most impressive robot dogs you should know, counting down to the most popular of all.

1. Ghost Robotics Vision 60

Let's start with the Vision 60 from Ghost Robotics. This one was built with defense and security in mind, which sets it apart from most quadrupeds.

It's designed to patrol rough terrain, carry specialized equipment, and operate where it may be too dangerous to send a person. It's exceptionally rugged, can be submerged in water, and has even gained an optional arm for manipulation. Its whole purpose is to keep people out of harm's way by going where they can't safely follow.

2. Deep Robotics X30

Where the Vision 60 focuses on security, the X30 from Deep Robotics is built for industry. It's a heavy, ruggedized quadruped, IP67-rated against dust and water, that inspects factories, power plants, and industrial sites.

It climbs stairs, clears obstacles, and navigates complex environments while collecting inspection data, and it's already deployed in jobs like high-voltage station inspection and underground cable tunnels. The pitch is simple: rather than sending workers into hazardous areas, a company can send a robot first.

3. ANYbotics ANYmal

Taking things further is ANYmal, developed out of ETH Zurich and commercialized by ANYbotics. This robot is known for its mobility in genuinely hard places.

It can navigate mines, tunnels, and offshore facilities, creating detailed 3D maps of locations that are difficult or dangerous for humans to reach. It's also been engineered for hazardous industrial settings, including an explosion-proof version certified for use around flammable gases, which is why energy companies have been early adopters.

4. Boston Dynamics Spot

Then there's Spot, the robot dog that introduced millions of people to modern robotics. Beyond the viral dance videos, Spot does serious work: construction, inspection, emergency response, and digital mapping.

With well over a thousand units deployed across dozens of countries, Spot is one of the most widely used quadrupeds in the world, and its mature software and sensor ecosystem have made it the benchmark others are measured against.

5. Unitree Go2

Finally, the Unitree Go2. What makes it remarkable is access: it brings capable quadruped hardware, with AI features like obstacle avoidance and autonomous following, down to a price hobbyists and universities can actually afford.

A built-in lidar and camera give it a 360-degree view, and an open SDK with ROS 2 support makes it a favourite for developers, researchers, and robotics enthusiasts. More than any other robot here, the Go2 is why so many people are now building with quadrupeds, and it's the same platform we've used to explore building a quadruped simulation in MuJoCo.

What these robot dogs have in common

Each of these robot dogs was built for a different job, from border patrol to factory floors to a developer's workbench. Together, they show how quickly quadruped robots are becoming practical tools across research, industry, and beyond.

They also share the same core challenge under the hood: staying balanced and moving reliably over unpredictable terrain, which is exactly why getting a robot to walk is so hard in the first place. If you want to experiment with the ideas behind these machines, the best place to start is simulation.

FAQ

What is the most popular robot dog?
The Unitree Go2 is among the most popular by units in the field, thanks to its low price, capable AI features, and open SDK. Boston Dynamics Spot is the most widely deployed in enterprise settings, with well over a thousand commercial units across dozens of countries.

What are robot dogs used for?
Common uses include industrial inspection of factories and power plants, security and defense patrols, mapping mines and tunnels, construction site monitoring, and emergency response. They excel at reaching stairs, rubble, and hazardous areas that are unsafe for people.

How much does a robot dog cost?
It ranges enormously. Consumer and research models like the Unitree Go2 start in the low thousands of dollars, industrial platforms like the Deep Robotics X30 run into the tens of thousands, and premium or defense systems like Spot and the Vision 60 can reach well into six figures.

Which robot dog is best for developers?
The Unitree Go2 is a top choice for developers and researchers because of its affordable price, onboard sensors, and open SDK with ROS 2 support. It's widely used for education, research, and prototyping robotics applications.

Why are quadruped robots useful compared to wheeled robots?
Legs let quadrupeds handle stairs, ladders, rubble, and uneven or confined terrain that wheels can't, which is why they're valued for inspection and response in places built for humans rather than machines.

Can you experiment with these robot dogs in simulation?
Yes. Platforms like the Unitree Go2 have widely available simulation models, so you can prototype behaviors virtually before touching hardware. Tools like Drift generate quadruped simulations in MuJoCo from a natural-language prompt, which is a low-cost way to explore the locomotion and navigation ideas behind these commercial robots.

Adding Vision to a Franka Panda Robot in MuJoCo

Aditi Sharma — Mon, 27 Jul 2026 07:04:56 +0000

A robot arm can move with incredible precision. Without a camera, though, it has no idea what's actually in front of it.

This walkthrough adds a camera to a Franka Panda in MuJoCo, giving the arm the ability to see, and rather than editing the model by hand, we let Drift attach the camera and wire up the live feed from a single prompt.

Why a robot arm needs a camera

A Franka Panda knows its own joint positions with high accuracy, so it can repeat a memorised motion perfectly. What it can't do without vision is react to anything it wasn't told about in advance.

Add a camera and that changes. The arm can locate an object that isn't in a fixed position, tell one item from another, and adjust to a scene that looks different from last time. For any task outside a rigidly structured setup, vision is what turns a precise-but-blind arm into one that can respond to the world in front of it. It's the same shift we saw when adding a LiDAR to a mobile robot, just aimed at manipulation instead of navigation.

Adding a wrist camera with a single Drift prompt

Starting from the existing scene, we give Drift a simple instruction: add a camera to the Franka Panda arm in MuJoCo and display its camera feed.

Mounting the camera on the gripper, a wrist camera, is a common choice for manipulation, because the camera looks out from roughly where the hand acts. That gives the arm a close, first-person view of whatever it's reaching for.

How Drift attaches the camera and configures the feed

From that prompt, Drift updates the simulation, attaches a camera to the robot, and configures the live camera stream. The sensor is added to the robot description and wired so its output can be displayed as the simulation runs.

What would normally mean editing the model, placing the camera frame, and setting up the rendering by hand is handled in one step. If you're starting earlier, here's how to build the pick-and-place scene with this arm first.

Launching the live camera feed in MuJoCo

Time to launch. With the camera attached, we run the simulation and bring up the feed.

Now we can see exactly what the robot sees as it moves through the scene. The view shifts with the arm, tracking whatever the gripper is pointed at. That first-person perspective is the raw input every vision-based behavior builds on, and watching it move with the arm makes it clear what the robot has to work with.

What vision unlocks for a robot arm

Giving a robot vision is a small addition with a large payoff. It's the first step toward tasks like object detection, visual servoing, where the arm uses the camera feed to guide its motion in real time, and autonomous manipulation.

It's a simple change that opens up a whole new way for the robot to understand its environment. With vision in place, the next challenge shifts from moving accurately to deciding what to do with what it sees. That's where manipulation gets genuinely interesting. Add a camera, launch the feed, and start exploring what the arm can do once it can see. The full walkthrough is in the video above.

FAQ

How do you add a camera to a robot in MuJoCo?
You add a camera to the robot's description, position it, for example on the gripper, and configure the simulation to render its view. In this walkthrough, Drift attaches the camera to a Franka Panda arm and sets up the live feed from a single prompt.

Why mount the camera on the gripper?
A gripper-mounted, or wrist, camera looks out from near where the hand acts, giving the arm a close first-person view of whatever it's manipulating. This is a common setup for manipulation because it keeps the target in view as the arm approaches it. The Franka Panda model used here comes from the MuJoCo Menagerie collection.

What can a robot arm do once it has a camera?
Vision enables tasks like object detection, visual servoing (using the camera feed to guide motion in real time), and autonomous manipulation. It lets the arm find and react to objects instead of only repeating pre-programmed motions.

What is visual servoing?
Visual servoing is a technique where a robot uses live camera data to control its motion, continuously adjusting toward a visual target. It lets an arm home in on an object even if that object moves or wasn't in a fixed position.

What does Drift do when adding a camera?
Drift updates the simulation, attaches the camera to the robot, and configures the live camera stream, all from a natural-language prompt, so you can see what the robot sees without manually editing the model or rendering setup.

Automating Pick and Place With the Franka Panda in MuJoCo

Aditi Sharma — Sat, 25 Jul 2026 13:14:25 +0000

We already built the scene: a Franka Panda arm, a cube and a target. Moving the arm by hand is one thing. Can it run the whole sequence on its own?

This walkthrough automates pick and place with the Franka Panda in MuJoCo, asking Drift to generate the full sequence so the arm grasps, lifts, and places the object without any manual control.

From a manual arm to an automated pick-and-place task

Picking things up and putting them down sounds trivial, but it's one of the foundational skills in robotics. A full pick-and-place sequence has to move the arm to the object, align the gripper, close it at the right moment, lift without dropping, travel to the target, and release, each step depending on the one before.

Writing that motion logic by hand is fiddly. Getting the timing and positions right is exactly the kind of work that eats an afternoon, which is why automating the generation of it is useful.

The prompt: automate the sequence with Drift

Starting from the existing scene, we give Drift a simple prompt: create an automatic pick-and-place sequence for the existing Franka Panda arm in MuJoCo, and save it as a named script.

That's the whole instruction. There's no need to specify every waypoint or gripper command, since the goal is described at the task level and Drift works out the sequence to achieve it.

How Drift generates and integrates the motion sequence

From that prompt, Drift updates the controller, generates the required motion sequence, and integrates it into the existing simulation. The pick-and-place logic is written into a script and wired into the scene we already had, rather than replacing it.

What would normally be careful, hand-tuned motion code is produced in one step and left ready to run. If you're starting earlier, here's how to build the pick-and-place scene and how to install MuJoCo first.

Watching the Franka Panda pick and place on its own

Time for the real test. We launch the simulation and let it run.

The Panda arm moves toward the cube, grasps it, lifts it clear of the table, carries it across, and places it on the target location, all automatically. No manual joint adjustments, no step-by-step control. The task the scene was built for now runs end to end on its own.

Why pick and place is a foundational robotics skill

This is a simple pick-and-place task, but it's one of the most fundamental skills in robotics. Almost every practical manipulation job is some version of it: move to an object, grasp it, and put it somewhere useful.

Once a robot can reliably pick, move, and place objects, that skill scales toward real applications, from warehouse automation and order fulfillment to industrial assembly. With Drift generating the sequence, you get a working automated task quickly and can spend your time refining the grasp, testing new objects, or building toward more complex behaviors. The full run is in the video above.

FAQ

How do you automate a pick-and-place task in simulation?
You need a motion sequence that moves the arm to the object, grasps it, lifts and transports it, and releases it at the target. In this walkthrough, Drift generates that full sequence for a Franka Panda arm and integrates it into an existing MuJoCo scene from a single prompt.

What is a pick-and-place task in robotics?
Pick and place is a manipulation task where a robot grasps an object at one location and places it at another. It's a foundational skill behind many real applications, including assembly, packaging, sorting, and warehouse automation.

What is the Franka Panda used for?
The Franka Emika Panda is a seven-joint collaborative robot arm widely used in research and education. Its dexterity and readily available simulation models make it a common choice for manipulation and pick-and-place experiments.

Why is pick and place considered a fundamental robotics skill?
Because most practical manipulation reduces to it: reach an object, grasp it, and move it somewhere. Once a robot can do this reliably, the same capability underpins tasks from industrial assembly to logistics.

What does Drift generate for an automated pick-and-place task?
Drift updates the controller, generates the motion sequence for the pick-and-place task, and integrates it into the existing MuJoCo simulation as a script, all from a natural-language prompt, so the arm runs the full task automatically.

Why Robot Hands Are So Hard?

Aditi Sharma — Sat, 25 Jul 2026 12:47:24 +0000

A new robot hand goes viral every few months for how human it looks. Look past the demo reel and the same thing is true: nobody has really solved the robot hand.

Impressive hardware, but understanding why robot hands are hard means looking at what happens after the demo ends. Despite decades of work, dexterous manipulation remains unsolved, and most working robots today don't have hands at all.

Why most robots use grippers instead of hands

Walk onto almost any factory floor and you won't see hands. You'll see grippers: two-finger pincers, three-finger claws, and often just a suction cup.

There's a good reason for that. The parallel-jaw gripper is one of the most successful pieces of hardware in automation history: one actuator, one degree of freedom, almost nothing to break, calibrate, or get wrong in software. A human hand has roughly 27 degrees of freedom. That gap is the entire engineering problem in miniature.

Every actuated degree of freedom you add is a tax: another motor, another failure mode, another thing that slips out of calibration before a demo. And for a huge fraction of real tasks, the gripper is enough. If the object always arrives in the same place, you don't need fingers, you need a clamp that closes reliably ten million times. Most robots get by without many fingers because most robots work in worlds we built for them.

What makes grasping so difficult in the real world

Outside a factory, that structure vanishes. Every object has a different shape, weight, and texture, and it rarely shows up where you expect.

To pick something up, a robot has to perceive the object, choose where to grip it, and apply exactly the right amount of force. Too little and it slips. Too much and a paper cup or an egg is crushed. A person adjusts continuously without conscious thought, using a dense sense of touch that current robots don't come close to matching.

The hardware fights back too. A dexterous hand is one of the worst real-estate problems in robotics: 20-plus degrees of freedom packed into a hand-sized volume, light enough not to wreck the arm's dynamics. That leaves two painful options. Put the motors in the hand and it gets heavy, and mass at the end of an arm is the enemy of everything carrying it. Or run tendons from the forearm, the way a human hand does, and take on cables that stretch, wear, and snap. Add cost on top: research-grade hands have run as much as a car, and a single controller hiccup into a table can turn one into a paperweight.

The tactile sensing gap in robot hands

The deeper technical gap is touch. Available dexterous hands still fall well short of human hands, and tactile ability is where the shortfall is largest. A robot may see an object clearly and still have almost no sense of how it feels in its grip, whether it's slipping, or how the contact is changing as it moves.

There's also a data problem. Learning-based approaches need enormous amounts of high-quality manipulation data, and capturing how a human hand actually moves and touches things at that scale is difficult in itself. Much of this research happens in simulators like MuJoCo, where contact-rich tasks such as reorienting a cube in-hand can be practiced at scale, as covered in this survey of learning-based in-hand manipulation.

Simulation carries its own version of the problem. Contact dynamics, friction, deformation, and the exact moment a fingertip slips are where simulation and reality disagree most. The sim-to-real gap is a challenge everywhere in robotics, and in the hand it's at its worst.

Underactuation, the middle path between two fingers and five

Framing this as two fingers versus five is a false choice, and some of the most interesting work sits in between.

The key idea is underactuation: mechanical design that produces dexterity-like behavior without a motor for every joint. An adaptive gripper that passively conforms to whatever it touches gets you a large share of human grasping with a fraction of the actuators. You put the intelligence in the mechanism rather than the motors. Evolution did something similar, since a lot of human grasping is mechanically coupled rather than independently controlled. You may not need to actuate everything in order to do everything.

Redesign the world, or build robots that fit it?

That raises a bigger question than finger count. There are two paths forward, and the industry is pursuing both.

One is to redesign the world for robots: structured warehouses, fixtures, conveyors, and packaging standardised so simple grippers suffice. That approach works, and it's why deployed robotics succeeds today. You've moved the complexity into the environment.

The other is to build robots dexterous enough to fit the world we've already built. Door handles, drills, light switches, knives, zippers, jar lids. None of it was designed for a claw, and all of it was designed implicitly around five fingers and a thumb. In human spaces, the human hand is the universal adapter. There's a quieter argument for it too: the internet is full of footage of human hands doing things, so the closer a robot's hand is to ours, the better that demonstration data transfers and the easier teleoperation becomes. The anthropomorphic form factor stops being vanity and becomes a data decision.

The catch is that you can only redesign the places you control. You can rebuild a warehouse. You can't rebuild every home, hospital, and messy human space built up over decades around human hands. This is what humanoid robots are betting on, and it depends on solving the hand.

Why hands are the frontier of physical AI

AI is getting smarter every day, and models are getting far better at understanding instructions and recognizing objects. The gap now sits at the point of contact, in the moment a robot has to physically touch the world and get the force right. As our founder put it in a longer piece on this question, intelligence with nothing to act through is just text in a box. The hand is where physical AI becomes physical.

Until robots master their hands, embodied AI still has one of its biggest challenges ahead. It's also one of the most interesting places to work right now, because progress on hands unlocks a huge share of what people imagine robots doing.

If you want the longer argument behind this, read our founder's full piece on how many fingers a robot actually needs. And if you'd rather get hands-on, most of this research starts in simulation: try building a pick-and-place scene in MuJoCo and see how quickly grasping stops feeling simple.

FAQ

Why are robot hands so hard to build?
Because grasping requires perceiving an object's shape, weight, and texture, then applying exactly the right force to avoid dropping or crushing it. Robot hands also lack the dense sense of touch human hands have, and the training data needed to learn dexterous manipulation is scarce and difficult to collect.

Why do most robots use grippers instead of hands?
Grippers are cheaper, more reliable, and easier to control. In structured settings like factories, where the same object arrives in the same orientation repeatedly, a simple gripper handles the task perfectly without the cost and complexity of a multi-fingered hand.

What is dexterous manipulation?
Dexterous manipulation is a robot's ability to handle and reorient objects skillfully, typically with a multi-fingered hand. It covers tasks like adjusting an object within the grip, using tools, and handling unfamiliar shapes, and it remains an active area of research.

Why is tactile sensing important for robot hands?
Touch tells a robot whether an object is slipping, how much force it's applying, and how contact is changing as it moves. Vision alone can't provide that, which is why tactile sensing is considered one of the key missing pieces in dexterous manipulation.

What is underactuation in robot hands?
Underactuation means a hand has fewer actuators than joints, using mechanical design so fingers passively conform to an object instead of driving each joint independently. It delivers much of the benefit of a dexterous hand with far fewer motors, less weight, and fewer failure points.

How many fingers does a robot actually need?
It depends on where the robot works. In structured settings like warehouses, a one-degree-of-freedom gripper is often the correct answer. In homes, hospitals, and other spaces built around human hands, more dexterity becomes necessary, which is why the question is really about whether you redesign the world for robots or build robots to fit the world.

How are robot hands researched and tested?
A great deal of the work happens in simulation, where contact-rich tasks can be repeated at enormous scale without wearing out expensive hardware. Physics engines like MuJoCo are widely used for this, and tools like Drift generate the simulated robots and environments that experiments run in, while the manipulation policies themselves are trained on top.

Why NVIDIA Cares So Much About Robots?

Aditi Sharma — Thu, 23 Jul 2026 15:06:30 +0000

Say NVIDIA and most people think graphics cards. So why is one of the world's biggest AI companies pouring billions into robotics?

The short answer to why NVIDIA cares about robotics is that it sees the same opportunity it captured in AI: not the flashy end product, but the platform everyone else builds on.

What NVIDIA actually builds for robotics

While companies like Figure and Tesla build the physical robots, NVIDIA is building the AI, simulation tools, and computing platform that power many of them. It's a supplier-to-everyone strategy rather than a compete-with-everyone one.

NVIDIA frames this as "three computers": one to train a robot's AI, one to simulate and test it, and one to run it inside the robot itself. Own all three layers, and you power robots regardless of which company ends up building the winning hardware.

Isaac Sim and training robots in simulation

The middle layer is where a lot of the action is. Tools like NVIDIA's Isaac Sim let developers train and test robots in simulation before they ever touch real hardware, with a companion framework, Isaac Lab, focused on training robot policies at scale.

This matters because training robots in the real world is slow, expensive, and risky. A physical robot can only practice one attempt at a time, and mistakes can break it. In simulation, thousands of virtual robots can practice in parallel, safely, at a speed no physical setup can match. That sim-to-real approach is central to modern robot learning.

GR00T and foundation models for robots

The AI layer is GR00T, NVIDIA's open foundation model for humanoid robots. Rather than programming one task at a time, the goal is a general model that helps robots perform a wide range of real-world activities, and that developers can fine-tune for their own machines.

It's the same foundation-model idea that reshaped language and images, now aimed at physical robots, and it's one of several AI models changing robotics right now. Paired with NVIDIA's Jetson Thor chips for on-robot computing, it completes the loop from training to deployment.

Why NVIDIA's platform-first bet could pay off

Put the pieces together and the strategy is clear. Simulation makes robot learning faster and safer. Foundation models make robots smarter and more general. And NVIDIA's chips run underneath both. Together, these tools help robots learn faster, more safely, and at a much larger scale than real-world training alone allows.

The future of robotics depends on more than better hardware. It depends on better software, better simulation, and better AI, the layer that turns a machine into a capable robot. That's exactly where NVIDIA is placing its biggest bets, and why a company known for graphics cards is now one of the most important names in embodied AI.

FAQ

Why is NVIDIA investing in robotics?
NVIDIA sees robotics as the next major platform opportunity. Rather than building robots to compete with companies like Figure and Tesla, it builds the AI models, simulation software, and chips that many robots rely on, aiming to be the platform underneath the whole industry.

Does NVIDIA build robots?
Not the physical robots themselves, for the most part. NVIDIA focuses on the enabling layers: training and simulation software like Isaac Sim and Isaac Lab, foundation models like GR00T, and computing hardware like Jetson Thor that runs inside robots built by other companies.

What is NVIDIA Isaac Sim?
Isaac Sim is NVIDIA's simulation platform for robotics. It lets developers train, test, and validate robot AI in a realistic virtual environment before deploying to real hardware, which is faster, cheaper, and safer than training on physical robots.

What is NVIDIA GR00T?
GR00T is NVIDIA's open foundation model for generalist humanoid robots. It's designed to help robots understand and perform a wide range of tasks, and to be fine-tuned by developers, as part of a broader Isaac platform that spans data, simulation, and deployment.

Why is simulation so important for training robots?
Training in the real world is slow, expensive, and risky, since a physical robot practices one attempt at a time and can be damaged. Simulation lets many virtual robots train in parallel at high speed and zero physical risk, then transfer what they learn to real hardware.

Where do tools like Driftfit into this robotics stack?
NVIDIA supplies the underlying platform: simulators like Isaac Sim, foundation models, and chips. Driftworks one layer up, generating the simulation workspaces developers actually run from a natural-language prompt, across simulators like Gazebo and MuJoCo, with Isaac Sim support on the way. NVIDIA provides the engine; Drift helps you build what runs inside it.