Reproduce “Planning under Noise with Neural Operators” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Michael Chen, Hong Chul Nam, Xi Deng, Anima Anandkumar
PUN plans under noisy execution by predicting a goal-directed desirability field for each new map and using bounded local search to select feasible steps, replanning as the robot moves. In a reported 4D manipulator benchmark at noise level λ = 4, it reached the goal without collision in 95.00% of trials, versus 85.83% for RRT, with runtimes of 11.66 versus 38.31 ms/start under the tested settings.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.