The LATENT project page presents an academic research publication on humanoid robot tennis skill learning, with a strong emphasis on open access to research outputs (paper, code, video) through public repositories. While the content itself does not explicitly engage with human rights frameworks, the structural approach—providing free, unrestricted access to scientific knowledge and enabling community participation in research advancement—implicitly supports UDHR provisions related to freedom of expression, education, and participation in scientific and cultural life. However, the presence of Google Analytics tracking without visible privacy safeguards or transparency represents a structural compliance gap with Article 12 (privacy).
Rights Tensions1 pair
Art 12 ↔ Art 19 —Privacy (Article 12) is subordinated to information access (Article 19): tracking data collection occurs without transparency or consent, while research information is openly shared.
It is interesting to watch. The movements of the robot are robot-like. I mean, wtf, there were no robot playing tennis before, but I have an idea how a robot playing tennis would be like, and this video confirms my expectations. Sharp, unsure movements, a lot of hesitation, ...
Movies pictured robots like this long before this become possible, but how did producers guessed it?
Or maybe movies rendered different kinds of robots, but this video bring into my memory only those, that look like this. A kind of confirmation bias?
Why can some Temu humanoid robot do this sort of impressive, coordinated, high-speed thing, but Tesla Optimus completely sucks at everything unless they’re moving at 0.02m/s (and even then they’re not great)? Like, train this thing on the latent space of folding my clothes out of the dryer and I will send you my money.
This is so interesting. Especially since it's kinda weird to train a robot to mimicking human play. I wonder what a perfect robot what actually behave like.
It wouldn't need to split-step to activate muscles, the footwork would probably be minimal. I imagine a lot of different unusual looking swings to confuse human players, while still making perfect contact. It could make really late drop shots or even rotate the racket at the last moment for crazy angles.
We have just started ramping up practical use of imitation learning from human demonstrations in humanoids. A bigger development is that one or two projects are working on training foundational vision action language models based on large video datasets.
I think before the end of summer general purpose physical knowledge and capabilities will start to be demonstrated by one or more humanoid AI or robotics groups.
Maybe 18 months at the absolute latest.
I'm guessing by next year or 2028 there will be services where you can order a robot to come cook and or clean for you. By 2029 it should be quite affordable to get a humanoid on a short term rental.
Do we have any standard benchmarks for humanoids to do domestic tasks?
So this is all pretty much theoretical, but very tightly woven strictly bounded protocols to be brought to production-- perhaps an accelerated alternative to perceive a much sooner ETA of 18months...
Maybe its moreso about reaching out to the right people about this "white paper" worthy research.
AFAIK, billions of dollars are poured into tennis mechanics at the highest level.
Introduce this to the right group of people, I truly can see this funded to play Janik Sinner where he would pay as a service to play against his worst nightmare.
That seems like quite an extrapolation and an extraordinary statement. This is a single task, in a lab setting. What your describing are extremely open-ended tasks in people’s homes.
I agree that the movements look quite robotic (though not as much as you might expect), but I don't think any movies have depicted robots moving like that. A much more common depiction is moving only a single joint at a time.
> Sharp, unsure movements, a lot of hesitation, ...
I like these particular descriptors. Another I would add is holding poses unnaturally still. While waiting for the ball, the robot holds its racket extremely consistently relative to its body even while sharply turning.
" Do we have any standard benchmarks for humanoids to do domestic tasks?" The answer is yes. Steve Wozniak proposed the Coffee Test. See https://www.youtube.com/watch?v=MowergwQR5Y
It's actually very clever. Despite the apparent simplicity, no current model could pass it.
Re your forecasts, I think they are optimistic in terms of timing but not ridiculously so.
Ironically something like this could eventually make elite level tennis training cheaper and more accessible. Families of some top US juniors already spend $100k per year, much of that on 1:1 coaching. Some fraction could eventually be automated, at least for repetitive basic skills practice. Like the next level of a tennis ball machine.
The "AGI" (-ish) moment for AI was shoving Common Crawl into a transformer.
What's the animal intelligence (physical int.) equivalent of that? I don't think such a dataset exists? (e.g. NVidia is trying to compensate for that with simulated worlds, i.e. synthetic data)
The humans in the video shot easy balls to the robot, which returns more difficult balls. It's the human that is doing all the running. The robot is quite static. However with better software and better hardware is possible that the robot will be so fast that it will miss no ball, and so strong to return balls faster than any human can reach. So there is no need to play fine shots. That could be a goal if we want to provide automated training partners to humans. If we want to win games against humans, stronger and faster is more than enough.
Medium A: Research advances scientific and technological culture
Editorial
+0.35
SETL
+0.13
LATENT system represents advancement in scientific and technological achievement; publication and code release enable broad participation in scientific and cultural life.
FW Ratio: 57%
Observable Facts
Research addresses advanced robotics and machine learning, contributing to scientific knowledge.
Paper, code, and videos publicly available for scientific community engagement.
Multi-institutional collaboration demonstrates scientific community participation.
arXiv preprint enables peer review and scientific discourse.
Inferences
Open-access publication model ensures broad participation in scientific and technological advancement.
Code and data release enable other researchers to build upon and participate in scientific progress.
Public demonstrations enable cultural participation in technological achievements.
Medium A: Open access to research promotes freedom of movement and scientific exchange
Editorial
+0.30
SETL
+0.12
Research outputs (paper, code, video) are publicly accessible via GitHub Pages, supporting free movement and dissemination of information across borders.
FW Ratio: 67%
Observable Facts
Paper accessible via linked arXiv repository.
Code repository publicly accessible.
Video demonstrations available on-page.
No geographic access restrictions detected.
Inferences
Open-access model removes barriers to international knowledge movement and collaboration.
Decentralized hosting (GitHub) and open repositories support freedom of information flow.
Medium A: Implicit advocacy for scientific advancement and technological progress as human endeavor
Editorial
+0.20
SETL
+0.14
Content emphasizes development of advanced capabilities for humanoid robots and athletic skill transfer, framed around scientific progress and innovation without explicit rights language.
FW Ratio: 60%
Observable Facts
Page presents academic research on humanoid robot tennis skills with links to paper, arXiv, video, and code.
Institutional affiliations listed include five research organizations across China.
Google Analytics tracking script embedded in page header.
Inferences
The provision of open-access resources suggests commitment to scientific knowledge sharing, which aligns with UDHR principles of cultural and scientific participation.
Framing focuses on technological capability rather than human rights implications or societal impact.
Medium F: Frame emphasizes human-robot collaboration and learning from human athletes
Editorial
+0.15
SETL
+0.15
Content describes learning from human motion data and collaboration with human players, implicitly recognizing human dignity and capacity as teachers/performers.
FW Ratio: 50%
Observable Facts
Page describes 'learning from imperfect human motion data' and demonstrations of 'multi-shot rallies with human players'.
Google Analytics tracking enabled (G-QZ38WT2YPD) with no visible privacy policy or consent management, indicating potential data collection without explicit disclosure.
Terms of Service
—
No terms of service detected on-domain.
Identity & Mission
Mission
+0.10
Article 27
Academic research mission promotes scientific advancement and knowledge sharing, supporting cultural and scientific participation rights.
Content appears openly accessible via GitHub Pages; links to arXiv paper and code provide open access to research outputs, supporting right to participate in scientific advancement.
Ad/Tracking
-0.05
Article 12
Google Analytics present; tracking behavior not transparent to users, affecting privacy.
Accessibility
—
No accessibility statement detected; structural accessibility cannot be evaluated from provided content.