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

**Authors:** Yuhan Liu, Xinqi Liu, Zixuan Dong, Keith W. Ross, Shenji Wan  
**Lab:** NYU · **Published:** 3 Nov 2026  
**Links:** [arXiv](https://arxiv.org/abs/2611.05372) · [PDF](https://arxiv.org/pdf/2611.05372) · [Code](https://github.com/Tinaliu0123/PolicyIK) · [This page](https://omen-bit.github.io/ai-paper-daily/paper/2611.05372.html)

## Key numbers

- **93.5%**: …mean errors over the final second within 1 mm and 0.2° on 93.5% of held-out tasks
- **98.9%**: and a deployment protocol raises success to 98.9%
- **95.7%**: PolicyIK executes the end-effector commands of the π_0.5 VLA policy with 95.7% success

## 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. We present PolicyIK, a goal-conditioned reinforcement learning policy for high-precision IK. It 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 it to both reach the tolerance and stay within it. 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, and 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 π_0.5 VLA policy with 95.7% success. These results show that reinforcement learning can provide high-precision inverse kinematics for robot arms. Code is available at https://github.com/Tinaliu0123/PolicyIK.

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