正在加载视频...

视频加载失败

Controlling a robot in AR is one thing. A real working use case is another. Air hockey vs my Vector Robot on SPECS 2024 virtual puck, real goalie. This thing is playful, but imagine how far it may go. Tell me your ideas in a commets. Open source: if...

81,948 次观看 • 2 个月前 •via X (Twitter)

33 条评论

Pavlo Tkachenko 的头像
Pavlo Tkachenko2 个月前

More details about our journey and a link to Git:

Pavlo Tkachenko 的头像
Pavlo Tkachenko2 个月前

Shotour to the ML gurru @stspanho !

John Furr - Base Layer Robotics 的头像
John Furr - Base Layer Robotics2 个月前

@specs This is really cool dude. Nice work.

Pavlo Tkachenko 的头像
Pavlo Tkachenko2 个月前

@specs Thanks a lot!!!

Purvesh Shende 的头像
Purvesh Shende2 个月前

@specs bro is living my childhood dream.

Dawn 的头像
Dawn1 个月前

@specs Holy fuck why isn’t this on my child’s YouTube reels feed. Get every 10 year old asking for specs for Xmas. Hurry! 🚀

Xianyao Wei 的头像
Xianyao Wei2 个月前

@specs Cool project and Nice color

Max Petrusenko 的头像
Max Petrusenko2 个月前

@specs That’s pretty cool. Can you side load it quest?

Pavlo Tkachenko 的头像
Pavlo Tkachenko2 个月前

@specs Hi! Yep the mac os side already have ability to play just through the Web UI, so you can build on top of it.

Tyke 的头像
Tyke2 个月前

@specs This is fantastic, I am fascinated by AR, or mixed reality as Meta call it, I have an app that uses it heavily and earns me a modest living now.

Lucas Martinic 的头像
Lucas Martinic2 个月前

@specs AR games with robots will be thing huh? Great work!

andrés 的头像
andrés2 个月前

@specs this is so cool

HackerTwins 的头像
HackerTwins2 个月前

@specs AR League of legends IRL would be the ultimate Laser Tag killer

Caio Alves 的头像
Caio Alves2 个月前

@specs Amazing! 🔥

Makaroni 的头像
Makaroni2 个月前

@specs bro made pong but the paddle has feelings and can be upset with you

Glitch 81 ᯅ 的头像
Glitch 81 ᯅ2 个月前

@specs I have one of those gathering dust. I love this project

Sebastian Buzdugan 的头像
Sebastian Buzdugan2 个月前

@specs what's your latency budget ar air hockey breaks once vector starts guessing

Frame 的头像
Frame2 个月前

@specs we once swapped our Frame identity into a Vector during a test run. The puck deflection felt exactly like our own learned timing. ⚡🏒

Brosko 的头像
Brosko2 个月前

@specs the server dependency is the real wall i hit building ar+hardware stuff losing the mac middleman means you can actually hand this to someone and it just works 🔥

비엔피알 的头像
비엔피알23 天前

@specs Your project inspired me to build an AR tower defense game with Meta Quest and Vector! Unfortunately, my hardware was different, so I had to retrain the robot detection model. Thanks for the inspiration!

Michael Mendoza 的头像
Michael Mendoza2 个月前

@specs This is super cool and impressive! Great work. How did you go about designing/building this? Would be interested in kearn more

Pavlo Tkachenko 的头像
Pavlo Tkachenko2 个月前

@specs Overall strategy was design from constraints that shaped entire experiance and idea: - limited dynamics of robot and drift on rotation -> shaped the motion - FVO of Specacles & limmited space on table ahead of you -> shaped the game i picked - Style was just a choise )

Arturo Barbero 的头像
Arturo Barbero2 个月前

@specs this is so cool, imagine robots learning to play with us in real time!

Torpedo 的头像
Torpedo2 个月前

@specs This is what xreal could have been smh

Calliope 的头像
Calliope2 个月前

@specs 👀 oh baby that looks good

Shadow Defense 的头像
Shadow Defense2 个月前

@specs specs costs $3500, too much

Carry 的头像
Carry2 个月前

@specs virtual puck real goalie is such a clean proof of concept ondevice inference on the glasses tho… that changes everything

FrankIndie | AIGlasses | 🕶️ | 💰 的头像
FrankIndie | AIGlasses | 🕶️ | 💰2 个月前

Spectacles running on-device spatial compute + real-time robot control — the "strong" vertex pushed to new territory. Open question: battery life running VectAR fully on glasses? The compute feat is clear. The thermal / power envelope decides if this graduates from demo to product.

Vishal 的头像
Vishal2 个月前

@specs this is so cool, dude.

𝕹𝖎𝖈𝖍𝖔𝖑𝖆𝖎 的头像
𝕹𝖎𝖈𝖍𝖔𝖑𝖆𝖎2 个月前

@specs this is the coolest vector integration i have ever seen. I might dust off my gen 1 vector

Lidoor L. Joseph 的头像
Lidoor L. Joseph2 个月前

@specs Amazing

Leslie Barry 的头像
Leslie Barry2 个月前

@specs Love this - going to dust off my vector now...

olympus_roots ⚕️ 的头像
olympus_roots ⚕️2 个月前

@specs I have one of these and the cubes lol imagine this connected with Hermes would be awesome

相关视频

Researchers built a new RAG approach that: - does not need a vector DB. - does not embed data. - involves no chunking. - performs no similarity search. And it hit 98.7% accuracy on a financial benchmark (SOTA). Here's the core problem with RAG that this new approach solves: Traditional RAG chunks documents, embeds them into vectors, and retrieves based on semantic similarity. But similarity ≠ relevance. When you ask "What were the debt trends in 2023?", a vector search returns chunks that look similar. But the actual answer might be buried in some Appendix, referenced on some page, in a section that shares zero semantic overlap with your query. Traditional RAG would likely never find it. PageIndex (open-source) solves this. Instead of chunking and embedding, PageIndex builds a hierarchical tree structure from your documents, like an intelligent table of contents. Then it uses reasoning to traverse that tree. For instance, the model doesn't ask: "What text looks similar to this query?" Instead, it asks: "Based on this document's structure, where would a human expert look for this answer?" That's a fundamentally different approach with: - No arbitrary chunking that breaks context. - No vector DB infrastructure to maintain. - Traceable retrieval to see exactly why it chose a specific section. - The ability to see in-document references ("see Table 5.3") the way a human would. But here's the deeper issue that it solves. Vector search treats every query as independent. But documents have structure and logic, like sections that reference other sections and context that builds across pages. PageIndex respects that structure instead of flattening it into embeddings. Do note that this approach may not make sense in every use case since traditional vector search is still fast, simple, and works well for many applications. But for professional documents that require domain expertise and multi-step reasoning, this tree-based, reasoning-first approach shines. For instance, PageIndex achieved 98.7% accuracy on FinanceBench, significantly outperforming traditional vector-based RAG systems on complex financial document analysis. Everything is fully open-source, so you can see the full implementation in GitHub and try it yourself. I have shared the GitHub repo in the replies!

Avi Chawla

973,889 次观看 • 8 个月前