AI Safety Night SF is back 🔥
Last time: 150+ people, sold out.
What’s new:
- Theme: From Multimodal to Physical AI
- A poster session where YOU present
Tue, Oct 20 · San Francisco
Poster submissions close Oct 17
Hosting with our CFO, @Skye_Kim_25
Links below 👇
incredibly excited for the adoption trajectory of @robocurve.
the physical AI benchmarks and evals market will note a similar trajectory to the software AI benchmarks (from generalized tasks to economic tasks). as this continues to accelerate, and as deployment continues to be a crucial focus of my companies - a standard benchmark is needed to assess the performance of models in the real world.
Stand Up Bottles was one of the hardest tasks across all previous models, so it was surreal seeing Dream-1 being able to do this.
We will need more tasks to see what robotics models struggle with.
Introducing Embodied First Exam:
One line of code to run coding agents in 1,090 embodied tasks across 31 simulation suites and 44 robots.
Can your agent go far beyond pick-and-place:
Puzzles with spatial constraints;
Olympic sports with multi-robot collab;
Real-time eval, where the world won't pause;
Dynamic, agile, whole-body control;
AutoResearch with sim2sim
Try your own agent:
harbor run -d embodied-first-exam -a <your_own_harness> -m <your_own_model>
Dream-1 by @dreamlabs_ai beats every LLM in robotics while being faster than all of them. Dream-1 scores 1.3x higher than GPT-6 Astra Ultrafast while being 1.7x faster.
Ahead of launch, @robocurve evaluated Dream-1 independently. Result: a new Pareto frontier for robot control. 16x cheaper, 1.7x faster, and better performance (59 vs. 48 on RoboDojo-RC Tier 1) than the previous best alternative.
LLMs can plan. Robots still need a cheap, fast, and general System 1 to act.
Dream-1 by @dreamlabs_ai beats every LLM in robotics while being faster than all of them. Dream-1 scores 1.3x higher than GPT-6 Astra Ultrafast while being 1.7x faster.