Ropedia (@ropedia

Ropedia

41 posts

Ropedia

@ropedia_ai

Interactive Intelligence from Human Xperience

Singapore / Bay Area

Joined November 2025

Pinned
Ropedia
@ropedia_ai
Mar 17
Today Ropedia releases Xperience-10M at #GTC day 1 — World largest real human 4D interaction dataset at 10M scale. Each trajectory aligns: • visual observations • spatial structure • human motion • interaction dynamics • task semantics A new foundation for
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Ropedia
@ropedia_ai
Jun 4
Infrastructure isn’t the loudest layer of the AI stack. It’s the one that lasts. Great conversation at #EchelonSG2026 with our Founder & CEO @Frozen_Burning on why infrastructure startups build the strongest long-term moats. Building the data layer for the future of Physical
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Ropedia
@ropedia_ai
Jun 2
Building AI that understands and interacts with the real world remains one of the field’s most important challenges. We’re excited to share that our Chief Scientist, @liuziwei7 will be delivering an invited keynote at the Third Joint Egocentric Vision (EgoVis) Workshop at CVPR
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Ropedia
@ropedia_ai
Jun 1
Real-world intelligence starts with real-world data. We’re bringing Xperience-10M to @CVPR — helping build the data foundation for the next generation of Physical AI. Looking forward to connecting with researchers, builders, and industry leaders shaping the future of AI.
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Ropedia
@ropedia_ai
May 27
What makes a truly reliable spatial benchmark? Today, Ropedia is excited to introduce SpatialBench — a comprehensive benchmark framework for Spatial Intelligence. SpatialBench evaluates 42 models across: • 5 domains • 19 datasets • 500+ real-world scenes Spanning
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Ropedia
@ropedia_ai
May 22
An inspiring work on Physical AI: PhysX-Omni. It introduces the first unified sim-ready generation framework for rigid, deformable, and articulated objects, along with a diverse dataset and new benchmark. - Page: physx-omni.github.io - Code: github.com/physx-omni/Phy… -
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Ropedia
@ropedia_ai
Apr 25
Yann is absolutely right. A robot-rich future needs physical intelligence that understands the real world and anticipate consequences. We built the corpus that makes that possible. Xperience-10M — 10M Interaction Episodes, in the wild. 3D + time + interaction + consequence. Now
Yann LeCun
@ylecun
Apr 24
Replying to @ben_j_todd
1. I never said LLMs were not useful. They are, particularly with all the bells and whistles that are being added to them. I use them. 2. A robot-rich future can't be built with AIs that don't understand the physical world and don't anticipate the consequences of their actions.
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Ropedia
@ropedia_ai
Apr 15
Our CTO @hongfz16 demoed Xperience-10M dataset+ our wearable capture device at #Singapore AI & Robotics Demo Night last night! Packed room. Sharp questions. The physical AI data bottleneck is real — and we’re solving it. #PhysicalAI #EmbodiedAI #Robotics
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Ropedia
@ropedia_ai
Mar 30
We dropped our dataset last week. Just 10M frames of real hands touching real objects. 5 days later → 1.66M downloads Today → 2.12M and climbing Status → Most trending on @huggingface Turns out the entire Physical AI community has been starving for real-world data.
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Ropedia
@ropedia_ai
Mar 26
Ropedia team @Frozen_Burning @hongfz16 spoke with Former Deputy Prime Minister Heng Swee Keat of Singapore🇸🇬 about physical intelligence and real-world data at the launch of NTU × UOB Innovation Hub. Honoured to be invited by @NTUsg. More to come. #EmbodiedAI #PhysicalAI #AI
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Ropedia
@ropedia_ai
Mar 24
Calling everyone in Singapore, we will be at @NTUsg 's NBS building tomorrow — full team on site. Limited Ropedia merch 👕 Come find us. Follow @ropedia_ai. #embodiedai #physicalintelligence #robotics #singapore #tech
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Ropedia
@ropedia_ai
Mar 19
Hot take: "More data" won't fix AI. If the data is static, you just get a bigger model that still doesn't understand the real world. Intelligence lives in sequences. Not snapshots. That's why we're building systems that act in the physical world — long-horizon sequential
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Ropedia
@ropedia_ai
Mar 17
Today Ropedia releases Xperience-10M at #GTC day 1 — World largest real human 4D interaction dataset at 10M scale. Each trajectory aligns: • visual observations • spatial structure • human motion • interaction dynamics • task semantics A new foundation for
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Ropedia reposted
AK
@_akhaliq
Mar 17
Ropedia Xperience-10M is out on Hugging Face a large-scale egocentric multimodal dataset of human experience for embodied AI, robotics, world models, and spatial intelligence It contains 10 million experiences (interaction) and 10,000 hours of synchronized first-person
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