Tagged articles

Code as Policy

3 articles · Page 1 of 1

Machine Learning Algorithms & Natural Language Processing

Oct 25, 2026 · Artificial Intelligence

RAX: Robot Agents Self-Improve via Online Exploration, Lead SOTA by 37.4%

Harvard, NVIDIA, Berkeley, MIT, and Stanford researchers introduce RAX, a framework where a coding agent autonomously explores robot perception and control interfaces, writes executable programs from successful trials, and accumulates reusable skills—achieving 37.4% average gains over prior state-of-the-art on five benchmarks without any human demonstrations or model fine-tuning.

Code as PolicyMulti-EmbodimentOnline Exploration

0 likes · 15 min read

RAX: Robot Agents Self-Improve via Online Exploration, Lead SOTA by 37.4%

Machine Heart

Oct 25, 2026 · Artificial Intelligence

RAX: Scaling Online Exploration Beats SOTA by 37.4% Across 5 Robot Benchmarks

Harvard, NVIDIA, Berkeley, MIT, and Stanford researchers introduce RAX, a framework that scales online exploration for robot agents, achieving 37.4% average improvement over state-of-the-art on five benchmarks without any human demonstrations, using frozen model parameters and self-generated code skills.

Code as PolicyMulti-EmbodimentOnline Exploration

0 likes · 15 min read

RAX: Scaling Online Exploration Beats SOTA by 37.4% Across 5 Robot Benchmarks

Data Party THU

Aug 19, 2026 · Artificial Intelligence

Weights vs. Skills: How Robot Learning Shifts from Action Prediction to Self-Written Skills

This survey maps a decade of robot‑learning research onto a weight‑vs‑skill axis, classifies 77 representative systems into six technical branches, analyzes their trade‑offs, highlights emerging skill‑economy challenges, and proposes measurable metrics for future self‑improving robotic systems.

AI roboticsCode as Policyrobot learning

0 likes · 17 min read

Weights vs. Skills: How Robot Learning Shifts from Action Prediction to Self-Written Skills