SimEX: agents learn manipulation skills in imperfect simulation and transfer them to real robots
chris_j_paxton · x · 2026-11-12
RoboPapers episode 116 features SimEX (Jiaheng Hu, Steve Tod, et al.), a simulation-based auto-research framework where LLM-powered coding agents autonomously build a robust toolbox of skills inside simulation—even when the sim doesn't match the real world well—and then deploy those skills on real robots.
- Demonstrated real-world tasks include towel folding and plate manipulation
- Extends coding agents' long-horizon capabilities from computer tasks to real robotics
- Offers a practical sim2real path for agent-driven skill acquisition
More from Embodied
- Apple affirms Vision Pro commitment; glasses rumored to be in secret development — Scobleizer · 2026-11-12
- ContactBench 1.0: A Benchmark for Contact-Rich Robot Manipulation With Printable Assets — leto__jean · 2026-11-12
- AMD runs MolmoAct2 robot policy locally on a Ryzen AI Max+ 395 mini PC — DJiafei · 2026-11-12
- Open-Source CoRL Paper Browser: 2,100+ Robot Learning Papers, a Decade of Searchable Research — promptdom · 2026-11-12
- World Labs and AMD enter robotics, Li Fei-Fei appears at CoRL — breadli428 · 2026-11-12
- Feather Robotics' $33,000 semi-humanoid tried hands-on: 15 lb payload per arm — chris_j_paxton · 2026-11-12