Topic
Robotics
5 stories, newest first.
- 01Robotics18 Oct 2026
NVIDIA open-sources a self-improving robot agent
NVIDIA's GEAR lab (with UMich, UIUC, UC Berkeley and CMU) released ASPIRE v1.0 on GitHub on Oct 16 under Apache 2.0. An AI coding agent writes robot control code, inspects detailed execution traces, diagnoses failures, repairs and re-tests its programs, and saves validated fixes as reusable text skills: training is skill refinement, not gradient descent. On a real two-armed robot with GPT-6 Astra, across three Rubik's Cube episodes (the first unsolved, the next two solved), time per completed face turn fell from about 17 minutes to about 3 and tokens per turn from 11.3M to 2.0M. In simulation with Claude Opus 5.5 it averages 99% success across all 80 LIBERO-Pro tasks; on 10 LIBERO-Pro tasks success rises from 15% (GPT-6 Luna) to 99% (Claude Opus 5.5). Caveats: a symbolic cube solver computes the moves (the agent handles perception, grasping and turning), and NVIDIA says ASPIRE is not yet a fully autonomous real-world learner.
Source: NVIDIA GEAR (opens in a new tab) - 02Robotics16 Oct 2026
Physical Intelligence opens Pi API beta
Upload robot data, fine-tune a π model on managed compute, and run it via hosted inference. Early partners include Actor's wheel loader (no operator inside) and Almond's deburring robot after four days of iterating. Beta by application.
Source: Physical Intelligence (opens in a new tab) - 03Robotics14 Oct 2026
Gemini 4 Argon taught a humanoid cornhole from one video
In an internal test Argon turned a raw human video into simulation code; Apptronik's Apollo then sank 7 straight shots in a real-world trial. Google says Video-to-Sim-to-Real compresses weeks of simulation modeling into hours.
Source: Google (opens in a new tab) - 04Robotics13 Oct 2026
Natura-1: robot hands trained on 1M hours of human video
Nature Will's 50B mixture-of-experts model, pretrained on 1M+ hours of egocentric human video plus limited robot data, follows plain-language instructions zero-shot and drives 20-DoF dexterous hands in a 100 ms loop. Nature Will calls it early evidence.
Source: Nature Will (opens in a new tab) - 05Robotics13 Oct 2026
Frontier AI can't label robot video
Concept's Oct 12 eval with Mecka on 200 egocentric clips: Mecka's specialized labeling model hit 79.5% fully correct vs GPT-6 Astra (max) at 43.5% as the best frontier. Specialized models plus humans in the loop still win for robot training data.
Source: Concept (opens in a new tab)