Today, humans convert their labor to capital. Capital holders need labor (humans) to acquire more capital. When the price of inference for these robots becomes less than the price of labor then capital holders don’t need labor.
Obviously, AI impacts non-manual labor too, but a significant portion of the world population does manual labor.
Could you sleep easily knowing that you have one in your house? What if you oppose the political views of its creators?
Presumably that company could use the Gemini Robotics product to eliminate the remote controller.
If the robots stop when humans are too close, wouldn't that mean that robots for close interaction or handling of humans need a whole other level of control?
Nvidia also releases their Cosmos series models.
But in a physical robot? Yeah, that thing is going to punch me in the face, eventually. Or worse.
We have a finite plane of existence (Earth), with an infinitely-expanding consumer base (humans), and we want to add robotic competition to that finite plane of existence? The data center AI is more appealing (I guess) since it doesn't compete literally shoulder-to-shoulder with me. Why do we want dexterous robots when we have humans?
It's certainly not there yet for anything practical, there's also certain bits and structures that don't have accurate names during construction, and it is important to keep that in mind - so a robot is unlikely to understand what it means to say "put the left bit of this box onto this right bit" due to ambiguity, a human would understand that
Plus we have no good reliable accuracy testing data in most cases (most tests occur on a few demos, but that isn't a good representation of how must things work), popular benchmarks, such as libero have been saturated, and nearly everything gets 95% there, most companies and researchers have their own benchmarks here.
Plus companies lie alot, and do very dangerous things in thier videos, I.e. these robots should not be standing very close to humans, because of being dangerous.
There are also legitimate concerns of misuse of these robots that need to be accounted for, misuse does not have to be warfare, but can be as simple as confusing it while it is cutting tomatoes with a knife.
Turning doorknob is easy, and fail recovery is also being worked on, but we don't have reliable statistics anywhere on that. The hard part is on practical things, as in when placing bricks or attaching a part during manufacturing it needs to ensure that it is aligning everything correctly....and that's hard, while it is impressive, it is very irresponsible to keep humanoids at home (people are irresponsible when untrained), for example, lawnmowers injure about 6400 people a year...and that is not an everything machine.
Humanoids in general are...not appealing in specific, due to maintainable of joints, complexity, but robot arms in particular, expecially on wheels (check mobile aloha), are likely to be able to do tasks such as clean up in hotels, after a guest had left, or replace some cooks in restaurants (if their work is consistent)
I did professionally few prototypes with robots and progress is real yet very far from what the average customer would find reliably useful in menial tasks.
FWIW I do think https://rodneybrooks.com/why-todays-humanoids-wont-learn-dex... remains relevant, namely dexterity is also a hardware problem, grippers aren't hands. They even clarify "multi-finger dexterous manipulation remains challenging." and those aren't even fingers with a lot of sensors.
Ultimately I shouldn't have to trust my robots. If my roomba or my dishwasher go haywire they won't pinch my finger off. They physically can't listen to me or spy on me. These are good robots.
it depends. in order for my slow ass Roomba to clean my floors, I have to move a bunch of things out of the way and not use the room.
on the flip side, i think this "the robot cannot automate the whole task" thing is reductive too. suffice it to say, EVERYTHING matters, there honestly nothing that "honestly doesn't matter."
This is a complete non-starter for already-built apartment blocks, terraced homes, and even most semi-detached. It's an interesting but costly solution for new detached homes.
Humanoids are a useful form factor because the already-built human world is, definitionally, built for humanoids.
For businesses the bar for adoption is very low: If the thing can work repetitive jobs for 24 hours a day and replace 3 shifts, the purchase bar is nominally anything less than 3 x human salary if your budgeting horizon is 1 year. That's a high number, and probably fairly easy to achieve.
For homes, it's a very different bar. You'd have a hard time convincing most American families to purchase anything with a >$1000 price tag. Currently that's pretty much impossible for a humanoid.
I don't think we'll get household robots anytime soon. Hell, the only ones that can afford them will be the same that would hire human household help.
It would be bad to remove demand for humans from the economy, of course. Humans have inherent moral value, and so it's good that our current system gives them economic value as well. But there's more than one way to achieve that end, and the massive quantity on the good side of the scale suggests it may be worth investigating the others instead of opposing the advancement outright.
I question the premise that humanoid robots are "around the corner". I suspect this will turn out more like self-driving cars which are still a very slow burn.
Like cars, they'll likely purchase them through finance deals, with a monthly payment. Or perhaps even rent/lease them.
But maybe they'd rather deal with a robot than a human?
* bot: "im nervous. is that a microphone?"
* bot: "great job, you're a machine!" (said to another bot, a machine)
* bot: clicks "i am [not] a robot" button on a computer
That's the most Skynet thing ever.
LLMs aren't themselves hurting anyone, no matter how much certain people like to pretend otherwise, whereas an AI in a robot with significant motors in absolutely can and will. There are reasons industrial ones live in safety cages after all.
Expecting them to be like inverse kinematic driven digital dolls is wrong because the optimization won't be for matching that but something like "net reduce energy consumption" which for electric motors in multi joint arms will look a bit odd.
Is this the new "x technology is a year away" ?
Sure Tau is teleoperated, but teleoperating an agile movement like that is actually really hard to get right and still involves AI to keep the robot balanced. Tau is a lot closer to real deployment than Google, and when it performs useful tasks it is simultaneously collecting the data to eventually automate those tasks.
Humanoids are far from being remotely useful in real life scenarios, yet.
Fun fact: most (if not all) qugv can’t go reverse on a stairway.
That aside: if I'm lucky enough to find a person who's good and reliable, they might move away, switch jobs etc.
It has all the headaches that come with hiring and managing someone, because, well, it is exactly that... If I don't want to be a manager at work (been there done that, happy to let others do it and get the raise that comes along with it), I sure as heck don't want to do it at home.
My bet is that the final robotic revolution will use genetically modified human/animal bodies with replaced brains. You'll have to stretch your ethics a bit, but if you grow a bear genetically modified in a way that it has no consciousness or thought, it'll make a much better construction worker than any humanoid robot. You'll just need to wire it up with neuralink and then control it via LLM. Fast animals can be used to deliver packages, and giraffes for warehouses.
Just want to say, Deepmind is a great place to work and the only (Edit: one the few unique labs!) lab where you can move from large frontier models (Gemini), frontier open models (Gemma), robotics (what you see here), science (weather, biology, more) and basically any other topic related to intelligence. It's really an incredible place to be, with incredible people. Consider joining! And thank you for the enthusiasm here.
For a robot to be in my house it would have to run locally, there's no way I'm allowing one that runs in the cloud to operate my washing machine for example.
Machine vision should definitely be handled by ML, but motion actuation should be relegated in the realm of traditional PID style linear/nonlinear control. Again, the tech is cool, but the practical usefulness of using a full LLM as a controller will probably run into hardware limitations.
So the robots will need to be weak, so even weak people can overpower it. But then it loses a ton of it's most promising abilities.
I don't mean this like a "rogue robot" situation. I mean it like the robot gets confused, or someone sees a walking $10M lawsuit in their home.
It would be cool to have a robot that can be taught to drive the same way we might teach a teenager to drive.
The only thing that bothers me is what happens when robots finally automated "all the mundane day-to-day tasks" including our jobs, what is there left to do for common folks who are not geniuses working at Google/Anthropic/ChatGPT or occupy the C-Suite of these companies?
The difference between gpt 2 and 3 was insane. 2 could generate limericks when it wasn't repeating a word 300x. 3 could actually do some things. By comparison gemini robotics has hardly changed at all.
I will also point out that slow, non-fluid robotics is on a totally different level of difficulty from fast fluid motion. Asimov could walk pretty smoothly, but it didn't fall over because it used a very careful sequence that was never unbalanced; you could pause at any point without falling over. Move faster, like boston dynamics, and you need to account for the change in balance from your arms swinging... or rather, you need to be able to account for the rotational inertia etc from moving multiple masses along complex paths with multiple points of articulation at hundreds or thousands of times per second.
An algorithm to fold tshirts 90% of the time is easy. The cloth hangs down by gravity and you can just look for right angles (corners), find their coordinates with binocular matching, and move them to meet each other. Getting 99%, or folding them quickly, so that the fabric is actually moving instead of just hanging still- incredibly, incredibly more complex.
There is going to be so much pain from VLA malicious compliance. If the current gen of LLMs are anything to go by I can already see it being an hilariously massive problem. People are careless.
Ignoring quibbling about inference not being the only cost and other issues and just accepting the proposed end state: this is very good if capital is effectively democratized, and apocalyptically bad if it remains highly concentrated in a narrow class.
And if all goes bad, imagine a world in which you and your family are homeless and starve to death.
Luckily the politicians and business leaders in place today who are going to be responsible for navigating us to one of these outcomes are the adults in the room, very ethical, even keeled and not the least bit corrupt. So... we should be fine! /s
Gemini doesn't remember almost anything after 2-3 follow ups. I have to paste the same "system prompt" at the top of each message and it still doesn't understand it.
As for why we want dexterous robots, the obvious answer is to do things that we don't want to do. Things that are back-breaking, disgusting, dangerous, or just plain boring.
For example, you could store far more things out of easy human reach.
And talking of kids, these will have to be exceedingly safe, a 6ft machine falling on a child is probably a worry most households could do without.
I think house cleaning/chores require at least a "part-time" job's work of work for the average household (especially with children). We may be atypical, but my partner and I don't want to hire out a cleaning service and deal with the whole human component. But, I think we'd gladly pay 15k+ if it allowed us to take on the additional gainful workload. Even at a 20k price tag, I suspect we would likely have made up the difference in under a year.
You can pay over $500 for an automatic cat litter box. Robot vacuums can be cheap but run to $600 or so. Household appliances is a robust sector where people have a proven track record of spending a lot of money.
If the future home robots are any good, it saves you from buying a dishwasher and robot vacuum. It can replace a maid/cleaning service and gardener/lawn-mowing service. For anyone paying for those services, it pays for itself.
$1000 isn't that high. Probably about a third of American households have an appliance over $2000, which is still a massive market.
More seriously:
- I wouldn't feel obligated to 'pre-clean' anything. Who wants to come across as a thoughtless/careless slob whose personal habits amount to borderline-abusive demands on the cleaning staff? Not me... and where does that line get drawn, anyway?
- I wouldn't worry about stuff being picked up and misplaced
- I have never had problems with maids stealing stuff, but I know others have
- Cleaning can be initiated or postponed as needed, with no dependencies on someone else's schedule
If Rosie was the only output she would never happen. Rosie might happen as a side benefit of replacing American workers but only for the rich people.
The vast majority which may be replaced would probably be better off burning down the factory rather than celebrate their upcoming domestic helper.
Robots aren't a necessity, and for a price tag in the tens of thousands, most people will just mop their own floors and do their own laundry.
I am imagining a world somewhere between the movie Elysium and Oblivion.
July 30, 2026 Models
Carolina Parada
From feet to fingertips — we are teaching robots intelligent whole-body control, fine dexterity, and teamwork to complete a broad range of complex tasks
For decades, we’ve dreamed of robots that can seamlessly step into our world and lend a hand. Now, that vision takes a significant stride forward.
Most robots are pre-programmed or teleoperated for narrow, repetitive task sequences. They lack the ability to truly learn for themselves or adapt to unpredictable environments. Moreover, transferring learned skills from one robot body to another remains incredibly difficult. To take on the hardest problems at scale, robots of every shape and size need AI models giving them the ability to think, act, and interact intelligently to safely complete tasks.
We demonstrated how Gemini's multimodal understanding could drive real-world action with Gemini Robotics. Today, we are introducing Gemini Robotics 2 - the intelligence layer powering the next generation of truly adaptable robots. As it takes its first literal steps, this major advance unlocks intelligent whole-body control, advanced dexterity, and multi-robot collaboration.
Gemini Robotics 2 enables robots to reason through every movement, unlocking a broad range of tasks. For example, it can enable a humanoid to walk, crouch, stretch, and manipulate objects to clean up a cluttered room. It can even team up with other robots to finish the job faster. And this profound intelligence can also run locally on-device while seamlessly adapting to entirely new robotic bodies in just a few hours.
We are making this possible through three highly capable models:
Gemini Robotics ER 2, our reasoning model, is now available on Google AI Studio and in private preview on Gemini Enterprise Agent Platform. Our VLA and On-Device models are available to early-access partners. Read how to bring these models to your hardware on our Developer blog.
The world is built for human movements; it requires us to reach, bend, and balance in tight, cluttered spaces. While our previous models controlled the humanoid’s upper-body to achieve table-top tasks, Gemini Robotics 2 expands physical AI into whole-body motions.
For the first time, our model can now control entire humanoid robots, translating intent into intelligent whole-body control. For example, when controlling Apptronik’s Apollo 2 humanoid robot, we can ask it to “put the watering can into the green bin in the bottom shelf.” Apollo processes the instruction, walks to the table, and picks up the watering can, takes a few steps to the shelves, and places it precisely in its destination. While our robots have more to advance in movement speed, this is an important step towards the skills needed to complete more complex, real-world tasks that require whole-body coordination.
To be genuinely useful in our homes and workplaces, robots need finesse. Gemini Robotics 2 unlocks a new level of physical dexterity across different end effectors, whether a robot is using hands or grippers, enabling robots to be more useful than ever before.
The model can now control the five-fingered, 22 degree-of-freedom SharpaWave hand on the Apollo 2 robot to complete delicate actions like tying knots or sealing a ziplock bag. It can also operate standard two-fingered parallel grippers on a Franka Duo platform to perform complex dexterous tasks (e.g. tight packing). We are continuing to advance the level of precision and speed to achieve human-level dexterity.
Most real-world tasks require multiple steps over an extended period of time. To manage this complexity, our embodied reasoning (ER) model, Gemini Robotics ER 2, serves as the robot’s high-level brain, processing user instructions and communicating with humans. It observes the room, reasons about the steps needed to complete the task, coordinates with the VLA to carry out the actions, and tracks progress until the task is done. This setup allows robots to execute complex multi-step tasks, self-correct if a step fails, and generalize to novel situations and goals.
In this update, we are enabling robots to more reliably execute longer task sequences, lasting several minutes and involving hundreds of decisions. Gemini Robotics ER 2 now understands when tasks begin and end, and can pinpoint the moment key events occur, marking a step change in progress understanding.
Furthermore, we are introducing multi-robot collaboration. This enables different types of robots to communicate and work together to solve complex workflows a single robot could not do alone.
Many robotic applications need to operate without network latency or internet connectivity. Gemini Robotics On-Device 2 is built specifically to handle these constraints — it is our most-efficient vision-language-action model (VLA) optimized to run locally on robotic devices.
This model is natively multi-embodiment and inherits our advanced “motion transfer” techniques from Gemini Robotics 1.5. We can now adapt to new bi-arm robot embodiments with just a few hours of adaptation time, typically with less than 200 examples. This works even with new embodiments with drastically different shapes, sensors and degrees of freedom, as shown below with a diverse set of tasks being performed by the Dexmate, SO101, and Trossen platforms.
Safety is foundational to our robotics research. As robots gain more physical capabilities, we are committed to ensuring end-to-end safety and alignment. With each release, we’ve taken a multi-layered approach that combines traditional physical safety measures with robust AI safety frameworks.
Gemini Robotics 2 specifically advances robotics safety for navigating the uncertainty of the real world and collaborating alongside humans.
We’re introducing ASIMOV-Agentic, a new benchmark for agentic safety orchestration and uncertainty resolution. For example, it measures the embodied reasoning agent’s ability to refuse unsafe tool calls from a VLA.It also measures the agent’s ability to predict whether a task is possible and to proactively request human intervention when uncertain.
Additionally, with enhanced embodied reasoning, Gemini Robotics ER 2 is our safest robotics model to date in safety constraint following and human proximity benchmarks. It can better detect when humans are nearby, trigger safety tool calls and bring the robot to a safe stop if someone approaches too closely. This is a key requirement in collaborative safety standards. Read our Gemini Robotics 2: Safety Technical Report for more details.
Gemini Robotics 2 marks an important milestone on the path toward solving AGI in the physical world. Unlocking the true potential of robotics requires moving past single-task automation toward general-purpose intelligence. By building this core intelligence, our goal is to enable AI in the physical world that can work alongside humans to solve complex challenges.
Explore Gemini Robotics 2
Acknowledgements
This work was developed by the Gemini Robotics team: Abhijit Ogale, Abhishek Jindal, Adil Dostmohamed, Adrian Collister, Alan Thompson, Alessio Quaglino, Alex Bewley, Alex Hofer, Alex Taeho Kim, Alex X. Lee, Alex Zihao Zhu, Allen Chai, Amaris Paryag, Amit Hampaul, Amy Nommeots-Nomm, Amy Shen, Andre Araujo, Anirudha Majumdar, Anna Volosina, Annie S. Chen, Annie Xie, Anthony Brohan, Antoine Laurens, Arunkumar Byravan, Asaf Revach, Assaf Hurwitz Michaely, Baruch Tabanpour, Ben Moran, Benoit Landry, Bingyi Cao, Bogdan Mazoure, Brandon Hernaez, Brijen Thananjeyan, Bryan Anenberg, Caden Lu, Carl Doersch, Carolina Parada, Charles Shu, Chengda Wu, Christine Chan, Christy Koh, Chuyuan Fu, Claire Cui, Clare Lee, Claudio Fantacci, Connor Schenck, David Rendleman, Deepali Jain, Demetra Brady, Dennis Li, Dhruv Shah, Dimple Vijaykumar, Dirk Ehrlich, Divya Garikapati, Dmitry Kalashnikov, Dre Mahaarachchi, Dushyant Rao, Erik Frey, Fangchen Liu, Francesco Romano, Frankie Garcia, Gabor Simko, Gautam Salhotra, Giulia Vezzani, Grace Popple, Grace Vesom, Graziano Misuraca, Guangyao Zhou, Hagen Soltau, Hanzi Mao, Hao-Tien Lewis Chiang, Harris Chan, Hila Noga, Howard Zhou, Ian Storz, Idan Lev-Yehudi, Ignacio Rocco, Inessa Konstanz, Isaac Reid, Ishita Prasad, Ivan Kapelyukh, J. Chase Kew, Jacky Liang, Jake Varley, James Susilo, Jasmine Hsu, Jerad Kirkland, Jeremy Plassmann, Jessica Lo, Jie Tan, Jimmy Yan, Jingwei Zhang, Jinyu Xie, Jose Enrique Chen, Joshua Ainslie, Joss Moore, Juanita Bawagan, Junkyung Kim, Justin Lidard, Kanishka Rao, Kathryn Quinn Shea, Kaustubh Sridhar, Keerthana Gopalakrishnan, Ken Caluwaerts, Kenneth Oslund, Khimya Khetarpal, Konstantinos Bousmalis, Krista Reymann, Krzysztof Choromanski, Ksenia Konyushkova, Kun Zhang, Kunal Aneja, Laura Graesser, Leen Verburgh, Leonard Hasenclever, Li-Heng Lin, London Chappellet-Volpini, Lucie Kerley, Maria Attarian, Maria Bauza Villalonga, Marissa Giustina, Max McCabe, Meet Kirankumar Dave, Mehdi S. M. Sajjadi, Metin Tokosz-Exley, Michael Neunert, Michael Noseworthy, Michiel Blokzijl, Miguel Rivas, Mithun George Jacob, Mitsuhiko Nakamoto, Mo Dawoud, Mohan Kumar Srirama, Mohit Sharma, Mohit Shridhar, Muinat Abdul, Murilo F. Martins, Nathan Batchelor, Nicolas Heess, Niko Milonopoulos, Norman Di Palo, Oliver Groth, Ouais Alsharif, Padmini Copparapu, Parth Parekh, Paul Ruiz, Paul Wohlhart, Peide Huang, Peng Xu, Peter Pastor, Petko Yotov, Phil Duffy, Philemon Brakel, Rachel Sterneck, Rajkumar Vasudeva Raju, Ravin Kumar, Razvan Surdulescu, René Wagner, Reza Sanatinia, Robert Baruch, Robert Moreno, Rohan Thakker, Roland Hafner, Sajjad Zafar, Sally Jesmonth, Sam Haves, Saminda Abeyruwan, Sandy Han Huang, Scott Crowell, Seliem El-Sayed, Sergey Yaroshenko, Sergio Martinez Abad, Serkan Cabi, Sharath Maddineni, Shuang Li, Sichun Xu, Silvia Cruciani, Skanda Koppula, Skye Yang, Soo Sung, Stefan Welker, Stefani Karp, Stefano Saliceti, Steven Hansen, Stuart Bowers, Sumeet Singh, Svetlana Grant, Takahiro Miki, Takuma Yoneda, Thomas Buschmann, Thomas Lampe, Thomas Power, Thor Schaeff, Tim Hertweck, Tingnan Zhang, Todd McInally, Todor Davchev, Tong Zhao, Travers Rhodes, Tsang-Wei Edward Lee, Vika Koriakin, Vikas Sindhwani, Wenhao Yu, Wentao Yuan, Xiaolin Fang, Yahav Nussbaum, Ying Sheng, Ying Xu, Yuheng Kuang, Yuxiang Yang, Yuxiang Zhou
For their leadership and support of this effort, we’d like to thank: Jean-Baptiste Alayrac, Zoubin Ghahramani, Koray Kavukcuoglu and Demis Hassabis. We’d like to recognize the many teams across Google and Google DeepMind that have contributed to this effort including Legal, Marketing, Communications, Responsibility and Safety Council, Responsible Development and Innovation, Policy, Strategy and Operations, and our Business and Corporate Development teams. We’d like to thank everyone on the Robotics team not explicitly mentioned above for their continued support and guidance. Finally, we’d like to thank our partners: Apptronik, Boston Dynamics, and Agile Robots teams for their support.
I kind of understand where the feeling comes from, but this also saddens me. What happened to our generation? We're scared of people.
In the humanoid robot scenario, you'll get a surveillance device with a built-in microphone and camera beaming every intimate detail of your space to the highest bidder. Instead of getting one person you don't know very well in your space, you'll get thousands.
The torque density and price of actuators has fallen dramatically since Ben Katz's MIT work on mini cheetah. The actuators on the Unitree G1 based on that work are powerful for their size and near quasi-direct-drive. The motors on the BD E-Atlas are completely passively cooled and appear to have really good torque density. Actuators have never been improving faster than they are now.
"Dave, would you like to get the instructions on how to stitch that head back to the neck?"
I only wish I could convince some humans in my life to slow down and do things with care.
I do think robotics would come up with more safety mechanisms (provably safe motion planning etc) just because the risk is a lot more serious than LLMs spitting half-truths
So at some point you have to trust that the tech is safe. Both in terms of "robot won't go off the rails", but also in terms of hostile actors can't remotely take over your robot while you sleep.
In terms of sleeping, personally, I would like for the law to mandate that robots must have a physical off switch, in a very visible location, that physically disconnects power. The switch should be illuminated while in the ON position. What makes me a bit pessimistic there is that we don't even have laws to mandate webcam indicator lights (e.g. a very tiny red LED) must be ON in hardware.
Humanoid startup execs just don't talk about that.
- the relatively crude tactile and proprioceptive sensing apparatuses of robots when compared to humans
- the limited availability of multisensory, perception-action coupled training data
Genuinely curious!
On PID: the field has been stuck trying to do analytical/optimization-based control for decades, and end-to-end control has shown incredible performance (e.g., SoTA cost of transport in legged locomotion) and robustness (e.g., not falling over when stepping on a pile of leaves) - while being far scalable (in terms of how fast it is to get a new robot up and running). Which is not to say it's perfect but it seems like it's a step in the right direction.
Better yet, companies like Physical Intelligence are doing good with hierarchical ("fast-slow") architecture to address both the intelligence and latency fronts.
TBF fabric is much more difficult to simulate than a bipedal body is.
As I recall openai had mujoco playing soccer nearly 10 years ago. Obviously real world bodies are much more difficult but I'd be curious to learn why that is.
For income - who knows?
Are there any indications to think it's possible in our world?
And (regardless of the costs involved) if I were going to pay money to not do this stuff myself, I'd rather pay a human to do it because I know there are humans out there that could use the work and I value them much more than I value Google and other corporations making even more obscene amounts of money selling future e-waste.
Initially, sure. But the price will come down in time, just like with any other technology in history.
What happens when those ideas need to be scaled into real products, though? For instance, I can't really imagine Google being fully committed to manufacturing and selling robotic arms at scale.
Selling to businesses is much easier, 99% of businesses can afford 200K if it replaces 3 x 70K humans.
And Level 4 is already here, scaling up, while we're seeing the first real signs of Level 5 (Tesla FSD Supervised).
The progress here is staggering - I'm not sure why you're so cynical!
I do agree though the humanoid form is a dead end for robots. Just build giant cubes that process inputs and give outputs, like a dishwasher. Why wash dishes with meat wand tentacles or try to recreate meat wand tentacles when you can accomplish the job in a wholly different way with far greater efficiency...?
Where's the clothes foldeing cube? Analogous to the clothes washer and clothes dryer.... the clothes folder...
Why stop at dishwashing...? Sell an entire integrated robotic kitchen.
Google acting like a normal but competent company that's just chugging away, meanwhile the hype cycle is propping up its upstart competitors to truly ludicrous valuations.
I’m not even sure if you are joking there or haven’t thought your proposal through. Sure giraffes are tall, but they can barelly lift any weight. What use would a robotically controlled giraffe be in a warehouse?
> There has been no innovation in robotic actuators since Honda's Asimo.
I very much doubt this. If nothing else the MIT Cheetah’s actuators are a whole different ballgame compared to asimo’s actuators. (Backdriveability, variable stiffness) And then there is a lot of interesting work being done with combining elastic elements with the actuators.
It's interesting to learn actuators are "behind". I kept seeing cool stuff in the 3D printing space and thought there's a lot of progress. I'd love to learn more.
Well, as long as you don't mind the workers in the robot company's telemetrics department whacking it to your robot's video feed.
> Many robotic applications need to operate without network latency or internet connectivity. Gemini Robotics On-Device 2 is built specifically to handle these constraints — it is our most-efficient vision-language-action model (VLA) optimized to run locally on robotic devices.
That makes them extremely easy to sell to industry. This unlocks, in principle, a large chunk of the potential untapped industrial automation market that still relies on human labor because the ROI of redesigning production lines didn't make sense.
I do want to pick a nit in this one,
> and the only lab where
I do think Allen Institution for AI (AI2) is has coverage across most of these domains ( albeit their frontier isn't nearly so far out, hopefully the $152m NSF awarded them + Nvidia is a fruitful partnership there).
For example, robotics: MolmoBot, MolmoSpaces, MolmoAct, https://allenai.org/embodied-ai
Apparently prices on them have gone way up. The one he has now would cost something like $6K today.
1. Do we want to be governed by machines?
2. Do we want to relinquish our freedoms to the whims of corporations?
Isn't that OP's point? Engineering muscle and sinew is harder than coming up with the control software. The cheapest way to a robot thus emerges as just taking the natural stuff and adding an artificial brain to it versus trying to re-engineer the bones and muscles with metal and plastic.
More realistically it seems that llms in several years could help dramatically decrease costs of automation and make it available for more industries
Chugging away not releasing products or releasing ones worse than their competition typically. Not sure that’s what a normal competent company should be doing.
This is the same reason people prefer buying Tesla cars direct instead of having to make a deal at a dealership. If they all had high EQ maybe they'd prefer the dealership model after all.
People are too quick to forget racially-diverse Nazi soldiers and other hilariously incompetent stuff that has been going around their generative AI efforts just a couple of years ago. Google look like they finally getting their shit together under immense competitive pressure but I would keep my eye on them for a few more years before starting calling them "competent".
Just imagine, there’s a woman Alice who cleans your home once a month. On this particular day, she had a fight with her husband this morning on (something completely unrelated to you).
Do you expect her to check her emotions at the door when she enters your space? Or is she going to give really bad vibes while she is moving around your house?
As an example, you can have an automated washer, dryer, and folder, sure. But what if you wanted to automate the retrieval of dirty laundry and the delivery of clean laundry ? That would need to be some sort of robot to travel throughout an environment (fit through human sized areas, open doors, walk steps) to collect and deliver things. And if I have a robot roaming around the house, I would prefer to just buy one robot that could do many things rather than have to buy it to just collect things and more expensive machines as well.
Can a household robot provide as much utility as a car? If "yes", the market gets pretty big.
If I can get a robot replace a gardener who I pay $170/mo for 2 visits per month, i probably would. But I suspect that would kill lawn
This is my dream for a house chore robot. If I could dump hampers of clothes into a receptacle and get stacks of folded clothes out, I’d happily pay $1k. Household of 5; my kids each produce 3-4 sets of clothes a day (sleep, school, sports, after school). I run wash in AM so dryer finishes before 3pm (pg&e ToU) and then I’m focused on other things for rest of day. More often than not, I get to bed to find a near full hamper of clothes dumped where I sleep and then have to sort/fold/deliver. Bonus points if robot can sort different items to different stacks/bundles.
I don’t need AI/robotics to save me time from having to think, research, or code… I need AI/robotics to save me time so I can think, research, or code.
I'd gladly pay or finance a robot that could have dishes clean, laundry folded, and carpets vacuumed by the time our family got home.
It's easier to redesign the work to be robot-friendly than to deploy a humanoid robot and have it actually work.
(I'm being facetious here: nothing that transmits data out of the house is going to be deployed to run household robots, at least not in my house. Even if I didn't care, I can't make that call on behalf of family members or guests. Then there's the obvious concern that the robotics company would sell the data to my homeowners' insurance company, health insurance company, and who knows who else.)
I don't want a person I barely know regularly going all over my apartment. What if they discover... nevermind.
A robot, on the other hand? Hell yeah!
Not to say we won't get humanoid robots eventually, but I think there's probably some low hanging fruit for people to make some other kinds of solutions. Specialized robots for industrial environment, well-thought out appliances for the home.
It would be a bit surprising if the progression was Roomba -> humanoid robot.
A waymo is basically an industrial strength robot. It operates among inherently unpredictable humans already. There is no guarantee that it will never, ever hurt a human.
And yet in many cities around the world, you can call a waymo and ride it and society accepts it.
Furthermore, Gemini is just all around a less trustworthy and mature model, for many reasons. Very smart but lacking the precision and holistically exhibited in the more recent models from OpenAI and Anthropic. On the flip side, Google's work on Gemma is unmatched.
Seems to me you'd do better if you built the robots with robots that were specialized in building robots.
https://www.engadget.com/2225849/google-shuts-down-alphafold...
Competent is a stretch. Google's AI offering seems to be, once again, PM led–lots of constantly-changing brands being merged and deprecated with zero customer support or service.
They'll almost certainly be one of the survivors. Their technical competece is unmatched. But that doesn't mean they have a great product in the way both OpenAI and Anthropic do. (Outside their datacentres, which are a legitimate feat.)
So, yeah, for complex reasoning and sensory processing, LLMs are the correct choice, and Gemini is especially strong at spatial reasoning over ChatGPT/Claude. But for actual motion/actuation? LLMs are the wrong tool for the job, probably easier to have LLMs program a reusable workflow in a script for repeated tasks instead of invoking LLMs after the first time.
Asimo used BLDC motors with strain-wave gearing, which is pretty much standard today on high-end humanoid robots. The only thing that has happened is that these are much cheaper today, and might be slightly more optimized.
> You'd have a hard time convincing most American families to purchase anything with a >$1000 price tag...
Which I am skeptical of.
Great machines!
I pay $200 every 2 weeks to have a 3000 sq ft house cleaned.
That's about $5200 a year to fully clean a house. And it's a human, so they can also tidy up, clean out the refrigerator, do my laundry, water my plants, etc.
Automation has been rolling out for a long while now, and most of the low-hanging fruit of things that can easily be redesigned in a cost-effective way has been dealt with already. The role of these robots is to address all of the stuff that would have been automated by now if they could have.
Isn't that Checkers right from the dawn of reinforcement learning?
Also the process that’s running at 16MHz is not the same as a full VLA. VLA is much more expressive. You think the processor that is running the VLA system is running at 10Hz or some GHz?
Human deliberate movement runs around 2-10 Hz I think.
They only became a “necessity” once they went mainstream and the world started evolving with vehicles becoming a part of everyday life.
I can certainly see the same thing happening with residential robotics.
That being said, by the time the kinematic hardware needed to build a robotic housekeeper is available for home use, I don't think the brainpower is going to be a problem. There's nothing in Ex Machina that couldn't run on a couple of RTX 6000s.
I certainly don't judge you, but I wanted to offer my perspective.
Now I imagine a robot doing that with all its compassion and empathy (which, however close to zero, still seem to be greater than in many people at this point).
Your parents bathed you and changed your diapers, why can't you? Or did they hire a robot for that?
Getting shit done, details of implementation, elegance, humanity be damned - is all I see in AI crowd's attitude to life.
increasing your spend by 25% is a smaller change then increasing your spend by infinity%
Also, no one said it could male any robot until you tried to broaden the scope just now because you didn't want to admit you're wrong. This is an example of "bad faith" in a discussion.
My parents, aunt/uncle, grandparents, and one of my neighbors uses the same house cleaner.
If she stole from me, I’d immediately tell my family and she would lose basically all of her clients.
That makes it easier for me to trust her.
(It works similarly in NYC apartment buildings - a single cleaner often services many people in the same building, so they have a reputation to uphold)
I would never use random online cleaning services.
Or, if the stress of doing everything they do is too much, and interferes with your ability to work and do other things you once had the time to do.