Humanoid Robotics: what are the biggest challenges now?
In our humanoid robotics market deck, you will find everything you need to understand the market
SUMMARY
Humanoid Robotics: what are the biggest challenges now? The biggest challenges are generalising to unfamiliar situations, manipulating objects reliably, surviving long deployments, maintaining useful workflow speed, staying safe around people, and making the economics work outside tightly controlled jobs.
Humanoid robotics has crossed an important threshold: tens of thousands of units are now shipping, but most of that volume still comes from entertainment, research, data production and service uses. The industrial story is real, just much smaller than the headline shipment number suggests.
Walking is no longer the main bottleneck. The harder mobility problem is keeping balance and control while carrying, pulling, reaching, handling contact and reacting to unexpected changes at the same time.
Hands have become one of the clearest hardware constraints. The frontier has moved from simply grasping objects to managing touch, slipping, grip force and tiny adjustments reliably across thousands of repetitions.
Generalisation is probably the biggest software problem. Current humanoid AI can execute long learned sequences, but independent benchmarks still show meaningful drops when objects, scenes or task conditions move outside what the model has seen before.
The data race is becoming enormous because physical intelligence needs experience with messy real-world variation. Human video, teleoperation, simulation and deployed-robot data are starting to form one combined training stack rather than separate approaches.
Battery life looks more manageable than durability. Charging docks and automatic battery swaps can keep a robot working through shifts, while there is still little public evidence showing how fingers, cables, bearings, gearboxes and sensors behave after years of daily use.
Short demonstrations can badly overstate productivity. The numbers that will matter more are productive actions per hour, intervention rate, recovery time and total usable uptime across a real workflow.
Humanoid economics improve dramatically with utilisation. Warehouses and factories can justify expensive robots by spreading cost across long shifts, while home robots face a much harder equation if they only perform a small amount of useful work each day.
Mass production is moving fast enough that raw unit volume is becoming less interesting than quality. First-pass yield, supplier consistency, repair rates and cost reduction will tell us more about industrial maturity than another factory capacity announcement.
The deepest test is still flexibility. A humanoid becomes genuinely valuable when the same machine can move between different jobs with little engineering, recover when something changes and keep doing useful work for thousands of hours.
This market map, featured in our humanoid robotics market deck, highlights top companies and startups in the humanoid robotics market
Why does humanoid robotics suddenly feel much more serious?
Humanoid robotics feels much more serious today because the industry has moved from a small collection of prototypes to tens of thousands of shipped robots and a growing number of real deployments.
Counterpoint Research's latest H1 2026 tracking puts global humanoid shipments above 22,000 units, almost 300% higher than a year earlier. The firm expects more than 50,000 units for the full year. That already puts the industry at a completely different scale from the experimental fleets people were watching only a few years ago.
The mix behind those shipments keeps the excitement in perspective. More than 60% still went to entertainment, performance, data production and research. Service and guidance represented roughly 19%, intelligent manufacturing 13%, and warehousing and logistics 5%. Using Counterpoint's 22,000-unit base, manufacturing and logistics together account for only about 4,000 robots.
At the same time, the industrial side is getting harder to dismiss. Figure has returned to BMW with Figure 03 after completing a long Figure 02 deployment. Agility's Digit is working inside a live GXO logistics operation. Boston Dynamics has started manufacturing the production version of Atlas, with its first industrial deployments committed.
The shipment boom is real, but most of today's volume still sits outside hard industrial work. Humanoids have become real products before they have become broadly proven workers.
| Main humanoid use in H1 2026 | Share of shipments | Approximate units from 22,000 shipments |
|---|---|---|
| Entertainment, performance, data and research | More than 60% | More than 13,200 |
| Service and guidance | About 19% | About 4,200 |
| Intelligent manufacturing | 13% | About 2,900 |
| Warehousing and logistics | 5% | About 1,100 |
Are humanoid robots actually doing real work today?
Humanoid robots are already doing real work today, although the strongest evidence still comes from narrow factory and logistics jobs.
BMW gives us one of the cleanest examples. Figure 02 spent about 1,250 operating hours at BMW's Spartanburg plant, handled more than 90,000 sheet-metal components and contributed to production of more than 30,000 BMW X3s. BMW says the robots worked ten-hour shifts from Monday to Friday during the deployment.
BMW has since moved on to Figure 03. The new project in Spartanburg tackles parts sequencing in an assembly and logistics hall, a more complicated workflow where the robot has to manipulate components, reposition its body and pull a cart. That progression is useful: Figure started with a relatively controlled pick-and-place task and is now being asked to handle a workflow with more movement and variation.
Agility Robotics has another meaningful dataset. Digit has moved more than 100,000 totes at GXO's Flowery Branch facility. Agility says the deployment has expanded beyond transferring totes between an autonomous mobile robot and a conveyor to include stacking them at another floor location.
China adds scale. Counterpoint reports clustered deployments of AgiBot's G2 on production lines operated with Longcheer Technology and Joyson Electronics, alongside growing industrial deployments from other Chinese vendors.
Humanoids can already earn their place on a production floor when the task is repetitive, the environment is controlled and the object set is limited. We still have far less evidence for robots moving freely between dozens of unrelated jobs.
As this chart shows, and as featured in our humanoid robotics market deck, search interest in where to buy robots has been rising steadily
Is walking still one of the biggest humanoid robotics problems?
Basic walking has fallen well down the humanoid robotics problem list; the harder challenge now is moving reliably while doing useful physical work.
Unitree rates the G1 at more than 2 metres per second. Figure 03 reaches 1.2 metres per second. Boston Dynamics' production Atlas combines a 1.9-metre human-scale body with 56 degrees of freedom and a sustained carrying capacity of 30 kilograms.
Those specifications matter less than what the robots can now do while moving. Figure 03's latest BMW workflow has Helix coordinating the hands, arms, torso and feet while the robot handles parts and pulls a heavy cart. Digit routinely walks while carrying totes inside a live warehouse. Atlas has been designed around mobile material handling rather than stationary manipulation.
The remaining mobility problem appears when several things go wrong at once: an unexpected contact, an uneven load, a person crossing the path or an object shifting during manipulation. Smooth walking on a flat floor no longer tells us much about how good a humanoid really is.
Are robot hands the hardest hardware problem now?
Humanoid robot hands are currently one of the hardest hardware problems because useful manipulation requires tiny movements, accurate force control and reliable touch sensing inside a mechanism that also has to survive constant impacts.
Figure's Helix 02 demonstrations show where the frontier has moved. Figure 03 has autonomously unscrewed bottle caps, extracted a small pill from an organiser, pushed exactly 5 ml through a syringe and picked small metal pieces from clutter. Those tasks force the robot to control contact continuously rather than simply close a gripper around a rigid object.
The research community is attacking the same problem. The recent TactiDex benchmark was built specifically around tactile-guided dexterous manipulation because reproducing the shape of a human hand movement still leaves out the forces that make the movement work. Another recent project, HT-Bench, collected 10 million visual frames and 7.8 million tactile frames across 226 tasks to study how robot hands can combine vision and touch.
Cameras can tell a robot what an object looks like, but once fingers wrap around it, the robot also needs to detect contact, slipping and changes in grip force. More dexterity then means squeezing additional joints, actuators, wiring and sensors into a tiny mechanism that has to remain light and durable.
We would rank hands above ordinary walking today because manipulation still decides how many useful jobs one humanoid can perform.
If you want more recent data on this point, please see our latest humanoid robotics market report.
This chart, featured in our humanoid robotics market deck, illustrates yearly venture capital funding for humanoid robotics startups
Can humanoid AI handle a task it has never seen before?
Humanoid AI still struggles when a familiar-looking job changes in an unfamiliar way, which is probably the biggest software problem in the field today.
The progress is real. Figure's Helix 02 has completed sequences containing 61 ordered loco-manipulation actions with one learned system controlling the full body. Two Figure robots have also reset a bedroom in under two minutes while reacting to each other's movements without a central coordinator.
Recent independent benchmarks show why those demonstrations still leave plenty unresolved. Colosseum V2 tests vision-language-action models across 28 tasks and 13 categories under both familiar and unfamiliar conditions. Its researchers found that current models still lose substantial performance when objects, scenes or other parts of the task move outside the distribution seen during training.
That weakness becomes much more serious in robotics than in software. A physical mistake can mean dropping an object, hitting a fixture or losing balance. Factories reduce those surprises through controlled layouts and predictable parts; homes multiply them.
The real breakthrough will be a humanoid that handles small, unfamiliar changes without somebody collecting another dataset and retraining the behaviour.
Where will humanoid robots get enough training data?
Humanoid robots currently need far more physical training data than the industry naturally produces, which is why companies are building enormous new collection systems around everyday human activity.
Figure's latest Index project makes the size of the problem unusually visible. Figure says contributors across more than 100 countries have already uploaded over 16 million videos. The system is processing roughly 30 minutes of new footage every second, equivalent to about 43,200 hours per day. Figure had already paid contributors $15 million and says it plans to spend more than $1 billion on data and compute over the following twelve months.
The comparison with conventional robot datasets is striking. RoboTacDex, a recent academic dataset for dexterous humanoid manipulation, contains about 6,000 trajectories across 19 tasks, 23 skills and 22 objects. That can be valuable research data, yet a general household robot eventually has to deal with an almost absurd number of object and situation combinations.
Simulation helps multiply experience. NVIDIA says GR00T 1.7 was pretrained using roughly 32,000 hours of real robot data and another 8,000 hours of simulated data. Among those two categories, simulation therefore supplied about one-fifth of the training hours.
A pattern is starting to form: human video adds breadth, simulation adds scale, teleoperation provides cleaner demonstrations and deployed robots generate the experience that grounds the system in real physics.
This chart, featured in our humanoid robotics market deck, shows how Agility Robotics is capturing share in humanoid robotics
Can humanoid robots last through full shifts and years of work?
Humanoid robots can cover full workdays today with planned charging or battery swapping, while their ability to survive years of continuous use is still largely unproven.
The public runtime figures from major platforms cluster around a few hours. Unitree gives the G1 about two hours. Digit reaches up to four. Boston Dynamics rates Atlas for four hours in normal use and about two hours during heavy lifting. Figure rates Figure 03 at five hours.
Across those four robots, the median advertised runtime is four hours. Companies are increasingly handling that operationally. Digit can autonomously dock at its charger. Figure 03 can step onto a wireless charging platform and charge at up to 2 kW. Atlas can swap its own battery in under three minutes.
Long-term durability is harder to judge. BMW's Figure 02 deployment accumulated around 1,250 operating hours, equivalent to roughly 52 days of continuous runtime. Digit's more than 100,000 tote movements provide another useful durability marker, but commercial robots working multiple shifts for five years would accumulate well above 20,000 operating hours.
Figure is already treating this as a manufacturing problem. Every Figure 03 goes through more than 80 functional verification tests and burn-in exercises including squats, shoulder presses and jogging for thousands of cycles.
The industry has decent answers for getting through a shift. It has much less evidence about bearings, fingers, cables, gearboxes and sensors surviving years of daily physical work with little maintenance.
| Humanoid robot | Published runtime | Current workaround |
|---|---|---|
| Unitree G1 | About 2 hours | Quick-release battery |
| Agility Digit | Up to 4 hours | Autonomous charging |
| Boston Dynamics Atlas | 4 hours normal, about 2 hours heavy lifting | Autonomous battery swap in under 3 minutes |
| Figure 03 | About 5 hours | Autonomous 2 kW wireless charging |
If you want more recent data on this point, please see our latest humanoid robotics market report.
Are humanoid robots actually fast enough at work?
Humanoid robots can already move quickly on individual actions, while full-job throughput still trails the clean speed implied by short demonstrations.
Figure offers a good example. In its logistics work, Helix reduced average package handling time from around five seconds to 4.05 seconds while raising correct barcode orientation from roughly 70% to about 95%. At 4.05 seconds per package, the pure motion works out to almost 890 package manipulations per hour if that exact action ran continuously.
Real workflows contain far more dead time.
As seen above, Figure 02 moved more than 90,000 BMW components during about 1,250 operating hours. That comes to roughly 72 components per robot-hour across the actual deployment. The task was completely different from package orientation, so comparing 890 with 72 as though they were the same benchmark would be misleading. The size of the gap still shows what disappears from demo-style cycle times: walking, waiting, perception, repositioning, safety pauses, hand-offs and recovery.
A recent real-robot benchmark called PhAIL makes a similar point from the AI side. Across the vision-language-action systems it tested, the best model still took roughly seven times as long per operation as the human reference. The benchmark uses a conventional robot arm rather than a humanoid, so we should treat the number as evidence about current VLA control rather than a humanoid productivity score.
The metrics worth watching now are productive actions per hour, intervention rate, recovery time and total usable uptime.
This chart, featured in our humanoid robotics market deck, illustrates yearly funding for humanoid robotics startups
Are humanoid robots cheap enough to be worth buying?
Humanoid robots can already make financial sense in highly utilised industrial jobs, but the economics get ugly very quickly when the machine sits idle or needs frequent human help.
Agility Robotics recently gave unusually concrete assumptions in its public-market materials. The company estimates that an owned Digit could generate about $400,000 of cumulative revenue for Agility over an assumed five-year robot life, including the robot, deployment, software and maintenance. Its Robotics-as-a-Service model comes to roughly $500,000 over the same assumed life.
Agility also says it has more than $300 million in contracts for Digit v5 orders, largely under the RaaS model. Those orders cover 1,000 Digit v5 robots under a multi-year arrangement, which suggests that some large customers are already willing to commit serious money before humanoids have reached mature industrial scale.
Utilisation completely changes the math. If we take the roughly $400,000 five-year ownership-model spend and spread it over one eight-hour shift, five days a week, the payment to Agility alone comes to about $38 per operating hour. Twelve productive hours per day pushes that below $26. Twenty productive hours brings it near $15.
Those numbers exclude electricity, financing, insurance and other customer costs, so they are best treated as a simple utilisation test. Agility's own model compares Digit with a fully burdened human labour cost of $30.50 an hour and assumes very high weekly utilisation.
This helps explain why factories and warehouses are ahead of homes. A logistics robot can potentially work across multiple shifts. A $20,000 or $40,000 home robot that performs twenty useful minutes of chores each day faces much harder economics even with a far lower purchase price.
The humanoid body itself has to justify its cost too. A fixed industrial arm, conveyor or wheeled robot will often beat a humanoid on one repetitive job. Humanoids become interesting financially when the same machine can move between many jobs without a custom automation project each time.
That flexibility is the bet. Until robots can actually deliver it, buyers should compare humanoids with simpler automation rather than only with human wages.
| Productive use | Five-year hours at 5 days/week | $400,000 spread over those hours |
|---|---|---|
| 8 hours/day | 10,400 | About $38.50/hour |
| 12 hours/day | 15,600 | About $25.60/hour |
| 20 hours/day | 26,000 | About $15.40/hour |
If you want more recent data on this point, please see our latest humanoid robotics market report.
Can humanoid companies actually mass-produce these robots?
Humanoid companies can already produce robots in the thousands, but consistent high-quality manufacturing is now becoming the tougher test.
China is farthest ahead on raw unit volume. Counterpoint estimates that AgiBot shipped about 9,700 humanoids during H1 2026 and Unitree more than 7,000. Together, those two companies accounted for roughly three-quarters of global shipments.
Those numbers need the application context from earlier in the article: much of today's volume comes from smaller robots used in research, data collection, services and entertainment. Manufacturing thousands of compact research-oriented humanoids and manufacturing thousands of high-duty industrial machines create different quality requirements.
Figure gives us a detailed view of the ramp on the more complex end. The company says BotQ has produced more than 350 Figure 03 robots and increased final assembly speed from one robot per day to a demonstrated cycle of one robot per hour in under 120 days.
The interesting number is the yield. Figure reported end-of-line first-pass yield above 80%, compared with 99.3% on its battery line. It has also produced more than 9,000 actuators across over ten designs and added more than 50 inspection points during assembly.
An 80%-plus first-pass yield during an early ramp can be respectable. It also means a meaningful minority of finished robots still need additional work before passing the first time.
Manufacturing is moving out of the "can anyone build these at volume?" stage. The next benchmark is whether companies can push production toward automotive-style consistency while lowering cost, keeping suppliers under control and changing the design every year or two.
That combination is much harder than simply opening a robot factory.
This chart, featured in our humanoid robotics market deck, compares the main business model options for humanoid robot manufacturers
Can a factory really drop a humanoid in without rebuilding the line?
Humanoid robots fit existing factories better than many forms of automation, but current deployments still require real integration work around the robot.
The appeal of the human shape is easy to understand. Factory aisles, shelves, carts, work surfaces and tools already assume human height, reach and movement. A humanoid can theoretically inherit that infrastructure.
BMW's experience shows what happens once theory meets the floor. After the Figure 02 project in Spartanburg, BMW specifically mentioned revised safety concepts with extra barriers and partitions, along with improved 5G coverage inside the hall.
Figure 03's newer sequencing job pushes integration further because the robot works inside an assembly and logistics flow rather than around one simple component-loading station.
Software creates another layer. Boston Dynamics built Atlas around its Orbit platform so customers can connect robots with manufacturing execution systems and warehouse management systems.
The human-shaped body helps with brownfield factories, but connectivity, fleet management, safety zones, charging and workflow software still have to be built around it.
Can humanoid robots safely work right next to people?
Safety is one of the biggest obstacles to wider humanoid deployment today because these robots combine mobility, heavy hardware, powerful joints and increasingly autonomous decision-making.
The regulatory picture is still being built. OSHA currently states that it has no standards specifically written for the robotics industry. Existing machinery rules, risk assessments and voluntary robotics standards therefore carry much of the load in US workplaces.
OSHA's recently published review of 2024 manufacturing injury data identified 550 incidents involving robots. Those incidents cover industrial robotics broadly rather than humanoids, so they tell us more about the surrounding safety problem than about humanoid accident rates. OSHA found that workers involved in production, installation, maintenance and repair were among the most commonly affected groups.
The standards world is now adapting to mobile, self-balancing machines. ISO 25785-1, covering dynamically stable industrial mobile robots including bipeds, is currently at committee-draft stage. A new edition of ISO 13482 covering service robots and physical human-robot contact has reached final-draft stage.
Manufacturers are adding their own layers in the meantime. Atlas uses onboard sensing to detect nearby people and vehicles and can pause when someone enters its safety radius. Boston Dynamics has also added padding and reduced pinch points. Digit includes features such as a safety PLC, emergency stops and safety-rated control systems.
These protections are far easier to manage inside factories than inside homes. An industrial site can restrict access, define operating zones and train employees. A household robot may share a room with children, pets, visitors and objects that nobody has mapped.
The safety challenge grows with autonomy. The more freely a humanoid moves through human space, the less the system can depend on fences and carefully controlled surroundings.
If you want more recent data on this point, please see our latest humanoid robotics market report.
This chart, featured in our humanoid robotics market deck, shows the revenue mix across customer segments in the humanoid robotics market
Are home humanoids actually autonomous yet?
Home humanoids still depend on a mix of autonomous AI, tightly demonstrated capabilities and human backup, so genuinely independent household robots remain an early-stage product.
1X is unusually open about that limitation with NEO. The company currently offers the home robot for a $499 monthly subscription or $20,000 under its Early Access ownership option. NEO arrives with basic autonomous capabilities powered by 1X's Redwood model.
For harder chores, 1X offers Scheduled Expert Mode. A remote 1X operator can supervise the robot's movements, complete the task and help the system learn the behaviour.
That could be a smart way to launch. It gives customers useful assistance before the AI can handle every edge case and creates valuable training data from real homes. It also leaves a human operational layer behind some tasks.
Figure is pushing harder toward fully autonomous household behaviour. Helix 02 has demonstrated kitchen cleanup, living-room tidying, bedroom tasks and fine manipulation under one generalist control system. Figure's Index project is now gathering human activity at enormous scale to widen the range of behaviour the model can learn.
Published evidence today consists mainly of controlled demonstrations, company testing and early-access deployment plans. Large fleets of home humanoids running independently for thousands of hours have yet to give us the kind of reliability data we now have from simpler consumer devices.
The home is probably the harshest benchmark the industry can choose. It combines unfamiliar objects, delicate manipulation, people, stairs, soft materials, privacy concerns and constant environmental change.
Factories only need humanoids to become excellent workers at selected jobs. Homes eventually ask them to become generalists.
So what are the biggest humanoid robotics challenges now?
The biggest humanoid robotics challenges now are generalising to unfamiliar situations, manipulating objects reliably, surviving long deployments and turning all of that capability into safe, cheap productive work.
We would put basic walking below those problems today. Battery life also looks increasingly manageable through autonomous charging and swapping. Mass production has moved quickly enough that thousands of humanoids are already shipping, especially in China.
Manipulation and generalisation sit at the top because they determine how many jobs one robot can actually perform. The recent Helix demonstrations are much stronger than the pick-and-place robotics of a few years ago, while independent VLA benchmarks still show meaningful deterioration when conditions change. The explosion in tactile datasets and Figure's 16-million-video Index project point toward the same missing ingredient: robots still need much more experience of the messy physical world.
Reliability and throughput come immediately behind. A robot that succeeds 95% of the time can look excellent in a demo and become painful in a workflow repeated thousands of times. Industrial customers will care about intervention rates, productive hours, maintenance and real output much more than acrobatics.
Economics then decides how much imperfection the market will tolerate. High utilisation already gives some warehouse and manufacturing deployments a plausible path to ROI. General-purpose robots become far more valuable if the same machine can switch jobs with little engineering, and that flexibility still needs to be proven at scale.
Safety sits across every one of these challenges. Standards for dynamically stable industrial robots and modern service robots are still evolving just as humanoids begin sharing more space with people.
The field's hardest problem has changed. Building a humanoid that walks convincingly is already within reach of many teams. Building one that can walk into an unfamiliar situation, understand what went wrong, manipulate the right object, recover by itself and keep doing useful work for thousands of hours is the challenge that will decide whether humanoid robotics becomes a huge industry.
| Challenge now | How serious is it? | What would convince us it is being solved? |
|---|---|---|
| Generalisation and recovery | The biggest software problem | High success rates on unfamiliar tasks and environments without retraining |
| Hands and physical manipulation | One of the biggest hardware problems | Reliable handling of fragile, soft, slippery and unfamiliar objects over thousands of cycles |
| Reliability and maintenance | Still largely unproven at fleet scale | Tens of thousands of productive hours with low intervention and repair rates |
| Real workflow throughput | Improving, but demos hide downtime | Public output-per-hour and intervention metrics from long deployments |
| Economics | Already plausible in selected industrial jobs | Competitive lifecycle cost across several tasks rather than one fixed workflow |
| Safety around people | A major deployment constraint | Mature standards plus large-scale evidence from fenceless human-robot operation |
| Battery endurance | Important but increasingly manageable | Reliable autonomous charging or swapping with very little lost productive time |
| Mass production | Moving quickly | High yields, falling unit cost and consistent quality across thousands of complex robots |
If you want more recent data on this point, please see our latest humanoid robotics market report.
This chart, featured in our humanoid robotics market deck, shows how factory humanoid robot technology has evolved over time
OUR METHODOLOGY
The question behind this analysis is simple to ask and harder to answer: what are the biggest challenges in humanoid robotics now? We broke it into the dimensions that most directly determine whether humanoids can become useful at scale: deployment, mobility, manipulation, AI generalisation, training data, endurance, reliability, throughput, economics, manufacturing, factory integration, safety and home autonomy.
For each dimension, we prioritised recent evidence that shows what robots can actually do today. That meant sustained deployments, measured operating data, task output, independent robot benchmarks, manufacturing yields, official product specifications, customer-side reporting and current safety standards.
Different evidence was used for different questions. Company demonstrations were useful for locating the capability frontier, independent benchmarks for testing generalisation and consistency, customer deployments for seeing whether systems survive real workflows, and shipment data for measuring scale.
We kept measurements separate when combining them would create a false comparison. Short task cycle times were distinguished from full-workflow throughput, advertised battery endurance from long-term hardware reliability, manufacturing volume from manufacturing quality, and successful demonstrations from evidence that a robot can recover when conditions change.
Where the underlying figures allowed it, we calculated simple derived measures such as output per operating hour, median advertised runtime and cost at different utilisation levels. Those calculations make the evidence easier to compare; they are not presented as standardised industry benchmarks.
The final ranking comes from a point-by-point aggregation of the evidence across those dimensions. We gave more weight to problems that still restrict useful deployment across many tasks and environments, especially where public evidence of a scalable solution remains thin.
Key sources used for this analysis include Counterpoint Research on global humanoid shipments and application mix, BMW Group on Figure's factory deployment, Figure on the Figure 03 BMW workflow, Agility Robotics on Digit's 100,000-tote milestone, GXO on the commercial Digit deployment, Boston Dynamics on production Atlas, Figure on Helix 02, Colosseum V2, TactiDex, HT-Bench, Figure's Index project, RoboTacDex, NVIDIA on GR00T, PhAIL, Agility Robotics' SEC filing on Digit economics, Figure on the Figure 03 production ramp, OSHA's robotics injury review, ISO 25785-1, ISO 13482, 1X on NEO pricing and Scheduled Expert Mode, and Figure on Helix logistics throughput.
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