PolicyIK: High-Precision Inverse Kinematics for Robot Arms with Reinforcement Learning

TL;DR

PolicyIK uses goal-conditioned reinforcement learning to achieve high-precision inverse kinematics for robot arms, overcoming challenges like redundancy and singularities. It learns to correct pose errors through adaptive action gains and reward shaping, enabling sub-millimeter accuracy in simulation and superior performance on physical robots compared to classical IK methods.

Why It Matters

High-precision IK is critical for vision-language-action policies and robot embodiment transfer, but traditional methods require extensive tuning and are sensitive to dynamics. PolicyIK reduces reliance on hand-crafted controllers and enables robust, generalizable IK with minimal domain adaptation.

Key Findings

• PolicyIK achieves mean pose errors within 1 mm and 0.2° over the final second on 93.5% of held-out tasks in simulation.
• A deployment protocol boosts success to 98.9% in simulation and outperforms four classical IK controllers on physical hardware.
• PolicyIK enables 95.7% success in simulated pick-and-place tasks when executing commands from a VLA policy.