Reproduce “PolicyIK: High-Precision Inverse Kinematics for Robot Arms with Reinforcement Learning” 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
Yuhan Liu, Xinqi Liu, Zixuan Dong, Keith W. Ross, Shenji Wan
PolicyIK uses reinforcement learning to make feedback-driven joint-reference updates, retaining accumulated commands rather than only measured joint positions. On a fixed-base 7-DoF OpenArm, it achieved 93.49% ± 0.20% strict single-attempt success across 4,120 held-out tasks, using final-second mean errors within 1 mm and 0.2°. On hardware, it reached 54/60 tasks and held 43/60 within tolerances. Results apply to tested setups.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.