Will Laufer

Yaru Niu Verified account @yaru_niu · 1d

Introducing EgoX: a cross-embodiment learning framework that trains manipulation policies across diverse embodiments using data from humans, physical robots, and simulated robots.

We cover highly heterogeneous embodiments: humans, physical and simulated humanoids, wheeled bimanual robots, tabletop arms, and quadrupeds. Their end effectors include three- to five-fingered dexterous hands, two-fingered and parallel-jaw grippers, and a fixed tool.

One recipe, any embodiment.
egox-dream.github.io

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NVIDIA Robotics Verified account @NVIDIARobotics · 4d

A finer touch for humanoid robots. 🤖

At #CoRL2026, NVIDIA is introducing GR00T 2, an open world action model for humanoids, alongside new open industrial benchmarks from @RLWRLD_ai and @UCBerkeley built on Isaac Lab-Arena.

Explore how NVIDIA’s open world action models, scalable simulation tools and agentic workflows help researchers train, test and refine robot skills, from connecting cables to assembling small components.

Discover more ➡️ nvda.ws/4ixvdZV

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Danfei Xu Verified account @danfei_xu · Jul 8

A few big updates from EgoVerse.ai:

1. EgoVerse is coming to RSS'26! @ryan_punamiya will present the work, and @simar_kareer will give a keynote at the Data-Centric Robotics workshop.

2. EgoVerse is expanding with more industry partners, including @microagi, @LightwheelAI, and Trace Labs, with a growing pool of training-validated, permissively licensed data (hitting 10k hours soon).

3. We are building the ecosystem around egocentric intelligence: Through a new partnership with the @nvidia Inception Program, we are connecting data vendors with researchers who can put this data to work.

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Bryan Verified account @bryan_houlton · Jun 17

Introducing Horizon from @0rinlabs: the first long-horizon learning benchmark made from real agent logs

- SOTA is 21% on the hardest section
- 7-35M tokens of real agent history per task
- Models are hardly getting better on the hardest tasks
- Humans can score 100%

(1/7)

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