some serious talent density on the research team
- co-author of NVIDIA's Cosmos 3
- multiple papers at NeurIPS, ICRA, CoRL, best paper awards, etc.
- core contributors to Stanford's RoboNet and OpenAI's GDPVal
been ahead of the curve on human-to-robot transfer, WAMs, and RL in sim. their early work here will be consensus soon
calling it now - the RL market for robotics will be 10x the size of the LLM RL market
there’s simply way more to hillclimb in the physical world and exponentially more surface area for economic ROI
We tested leading video world models on their ability to predict simple physics and found that most fail within a few frames.
Today we're releasing MC-PhysBench, a new kind of benchmark that measures how long a model's output stays physically valid.
15+ models. 9 scenes. 6,000+ videos scored. 7 failure checks.
We spent the weekend experimenting with Argus and pushed it to SOTA on WGO-Bench.
Our optimized version scores 50.2% semantic F1, up from the 29.8% out-of-the-box score, and well ahead of other leading annotation services.
All of the gains came from how the model sees the video. We gave it denser frames, a second look at each action's boundaries, and a final check on every label.
Thanks to @PantheonInc@calixo888 for open-sourcing Argus.
The full repo is coming soon. For now, you can read our tech report here: https://t.co/Ue1gHmWoBT
Introducing MC-EgoHands, the first all-in-one harness for turning raw ego video into training-ready data.
→ Frontier hand pose (<7mm) across 4 public benchmarks
→ Human grasps retargeted to Panda/LIBERO and Yam in one pass
→ Occluded frames flagged as interpolated or missing
→ Structured episodes in @huggingface@LeRobotHF v3
Introducing Midcentury.
We’re building the data and simulation infra for physical AI.
Today, we’re coming out of stealth with a $15M Series Seed to scale robotics beyond polished demos.
We’re already supporting frontier labs with:
→ The world’s largest egocentric dataset: 2M+ hours, 50+ environments, 20,000+ tasks
→ Matrix: a frontier simulation platform scaled with our real-world data to evaluate and post-train policies at scale