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

Paper Overview

• Field: Machine Learning / Robotics
• Authors: Yuhan Liu, Xinqi Liu, Zixuan Dong, Keith W. Ross, Shenji Wan
• Published: 2026-11-03
• arXiv: 2611.05372
• Code: https://github.com/Tinaliu0123/PolicyIK

Abstract

Inverse kinematics (IK) converts end-effector pose commands into joint motion, and many vision-language-action (VLA) policies rely on it to act on a given arm and to transfer across embodiments. Precise IK remains laborious: redundancy and singularities complicate the inverse mapping, execution depends on hand-tuned controllers, and training data on physical robots are costly to collect. The authors present PolicyIK, a goal-conditioned reinforcement learning policy for high-precision IK.

Key Technical Contributions

• Updates joint-position references from pose feedback and observes its accumulated reference relative to the measured joints, so it can retain the offset that the position controller needs at rest.
• Two-scale pose errors with an error-dependent action gain resolve millimeter residuals while preserving large motions.
• Progress and dwell rewards teach the policy to both reach the tolerance and stay within it.

Results

• In simulation, a single attempt by the trained PPO policy keeps the mean errors over the final second within 1 mm and 0.2° on 93.5% of held-out tasks; a deployment protocol raises success to 98.9%.
• When trained directly on a physical arm with an off-policy RL learner, PolicyIK achieves higher reaching and holding success than all four classical IK controllers under the same time budgets.
• In simulated pick-and-place, PolicyIK executes the end-effector commands of the \(\pi_{0.5}\) VLA policy with 95.7% success.

These results show that reinforcement learning can provide high-precision inverse kinematics for robot arms.

--- *Automatically collected on 2026-11-05.*